INFO 2025-06-19 06:36:05,868 instructlab.model.evaluate:584: Using local model found at '/var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397' for '--model' INFO 2025-06-19 06:36:05,886 instructlab.model.evaluate:584: Using local model found at '/var/home/rhcert/.cache/instructlab/models/prometheus-8x7b-v2-0' for '--judge-model' INFO 2025-06-19 06:36:06,294 numexpr.utils:146: Note: detected 128 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable. INFO 2025-06-19 06:36:06,294 numexpr.utils:149: Note: NumExpr detected 128 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16. INFO 2025-06-19 06:36:06,294 numexpr.utils:162: NumExpr defaulting to 16 threads. INFO 2025-06-19 06:36:06,978 instructlab.model.evaluate:189: Generating answers... WARNING 2025-06-19 06:36:07,096 instructlab.model.evaluate:773: Using gpus from --gpus or config and ignoring --tensor-parallel-size configured in serve vllm_args INFO 2025-06-19 06:36:07,882 instructlab.model.backends.vllm:115: Trying to connect to model server at http://127.0.0.1:8000/v1 INFO 2025-06-19 06:36:09,108 instructlab.model.backends.vllm:332: vLLM starting up on pid 21 at http://127.0.0.1:50913/v1 INFO 2025-06-19 06:36:09,108 instructlab.model.backends.vllm:123: Starting a temporary vLLM server at http://127.0.0.1:50913/v1 INFO 2025-06-19 06:36:09,108 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 1/120 INFO 2025-06-19 06:36:12,417 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 2/120 INFO 2025-06-19 06:36:15,804 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 3/120 INFO 2025-06-19 06:36:19,290 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 4/120 INFO 06-19 06:36:19 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:36:20 [api_server.py:1034] vLLM API server version 0.8.4 INFO 06-19 06:36:20 [api_server.py:1035] args: Namespace(host='127.0.0.1', port=50913, uvicorn_log_level='info', disable_uvicorn_access_log=False, allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template='/tmp/tmpm423pemf', chat_template_content_format='auto', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, enable_ssl_refresh=False, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_request_id_headers=False, enable_auto_tool_choice=False, tool_call_parser=None, tool_parser_plugin='', model='/var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397', task='auto', tokenizer=None, hf_config_path=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, allowed_local_media_path=None, load_format='auto', download_dir=None, model_loader_extra_config=None, use_tqdm_on_load=True, config_format=, dtype='auto', kv_cache_dtype='auto', max_model_len=None, guided_decoding_backend='auto', logits_processor_pattern=None, model_impl='auto', distributed_executor_backend='mp', pipeline_parallel_size=1, tensor_parallel_size=8, data_parallel_size=1, enable_expert_parallel=False, max_parallel_loading_workers=None, ray_workers_use_nsight=False, disable_custom_all_reduce=False, block_size=None, enable_prefix_caching=None, prefix_caching_hash_algo='builtin', disable_sliding_window=False, use_v2_block_manager=True, num_lookahead_slots=0, seed=None, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_partial_prefills=1, max_long_partial_prefills=1, long_prefill_token_threshold=0, max_num_seqs=None, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, hf_token=None, hf_overrides=None, enforce_eager=False, max_seq_len_to_capture=8192, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, disable_mm_preprocessor_cache=False, enable_lora=False, enable_lora_bias=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=['samples_2397'], qlora_adapter_name_or_path=None, show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, scheduling_policy='fcfs', scheduler_cls='vllm.core.scheduler.Scheduler', override_neuron_config=None, override_pooler_config=None, compilation_config=None, kv_transfer_config=None, worker_cls='auto', worker_extension_cls='', generation_config='auto', override_generation_config=None, enable_sleep_mode=False, calculate_kv_scales=False, additional_config=None, enable_reasoning=False, reasoning_parser=None, disable_cascade_attn=False, disable_chunked_mm_input=False, disable_log_requests=False, max_log_len=None, disable_fastapi_docs=False, enable_prompt_tokens_details=False, enable_server_load_tracking=False) INFO 2025-06-19 06:36:22,591 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 5/120 INFO 2025-06-19 06:36:25,777 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 6/120 INFO 2025-06-19 06:36:29,040 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 7/120 INFO 2025-06-19 06:36:32,251 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 8/120 INFO 2025-06-19 06:36:35,616 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 9/120 INFO 06-19 06:36:36 [config.py:689] This model supports multiple tasks: {'embed', 'classify', 'reward', 'generate', 'score'}. Defaulting to 'generate'. INFO 06-19 06:36:36 [arg_utils.py:1742] rocm is experimental on VLLM_USE_V1=1. Falling back to V0 Engine. WARNING 06-19 06:36:36 [arg_utils.py:1603] The model has a long context length (131072). This may causeOOM during the initial memory profiling phase, or result in low performance due to small KV cache size. Consider setting --max-model-len to a smaller value. INFO 2025-06-19 06:36:38,948 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 10/120 INFO 2025-06-19 06:36:42,105 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 11/120 INFO 2025-06-19 06:36:45,333 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 12/120 INFO 2025-06-19 06:36:48,636 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 13/120 INFO 2025-06-19 06:36:51,885 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 14/120 INFO 06-19 06:36:53 [api_server.py:246] Started engine process with PID 69 INFO 2025-06-19 06:36:55,137 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 15/120 INFO 06-19 06:36:57 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:36:58 [llm_engine.py:243] Initializing a V0 LLM engine (v0.8.4) with config: model='/var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397', speculative_config=None, tokenizer='/var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=8, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='auto', reasoning_backend=None), observability_config=ObservabilityConfig(show_hidden_metrics=False, otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=None, served_model_name=samples_2397, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=None, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"max_capture_size":256}, use_cached_outputs=True, WARNING 06-19 06:36:58 [multiproc_worker_utils.py:306] Reducing Torch parallelism from 64 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed. INFO 2025-06-19 06:36:58,413 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 16/120 INFO 2025-06-19 06:37:01,726 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 17/120 INFO 06-19 06:37:04 [__init__.py:239] Automatically detected platform rocm. INFO 2025-06-19 06:37:05,072 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 18/120 INFO 06-19 06:37:05 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:37:05 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:37:05 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:37:05 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:37:05 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:37:06 [__init__.py:239] Automatically detected platform rocm. (VllmWorkerProcess pid=95) INFO 06-19 06:37:06 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=92) INFO 06-19 06:37:06 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=97) INFO 06-19 06:37:06 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=91) INFO 06-19 06:37:06 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=94) INFO 06-19 06:37:07 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks INFO 2025-06-19 06:37:08,282 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 19/120 (VllmWorkerProcess pid=93) INFO 06-19 06:37:08 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=96) INFO 06-19 06:37:11 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks INFO 2025-06-19 06:37:11,626 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 20/120 INFO 2025-06-19 06:37:14,983 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 21/120 INFO 2025-06-19 06:37:18,286 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 22/120 INFO 2025-06-19 06:37:21,599 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 23/120 INFO 2025-06-19 06:37:24,825 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 24/120 INFO 2025-06-19 06:37:28,148 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 25/120 INFO 2025-06-19 06:37:31,400 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 26/120 INFO 2025-06-19 06:37:34,772 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 27/120 INFO 06-19 06:37:36 [rocm.py:153] None is not supported in AMD GPUs. INFO 06-19 06:37:36 [rocm.py:154] Using ROCmFlashAttention backend. INFO 2025-06-19 06:37:38,111 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 28/120 INFO 2025-06-19 06:37:41,258 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 29/120 INFO 2025-06-19 06:37:44,506 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 30/120 INFO 2025-06-19 06:37:47,658 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 31/120 INFO 2025-06-19 06:37:50,931 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 32/120 INFO 2025-06-19 06:37:54,353 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 33/120 INFO 2025-06-19 06:37:57,795 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 34/120 INFO 2025-06-19 06:38:01,012 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 35/120 (VllmWorkerProcess pid=95) INFO 06-19 06:38:03 [rocm.py:153] None is not supported in AMD GPUs. (VllmWorkerProcess pid=95) INFO 06-19 06:38:03 [rocm.py:154] Using ROCmFlashAttention backend. (VllmWorkerProcess pid=92) INFO 06-19 06:38:04 [rocm.py:153] None is not supported in AMD GPUs. (VllmWorkerProcess pid=92) INFO 06-19 06:38:04 [rocm.py:154] Using ROCmFlashAttention backend. INFO 2025-06-19 06:38:04,368 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 36/120 (VllmWorkerProcess pid=97) INFO 06-19 06:38:05 [rocm.py:153] None is not supported in AMD GPUs. (VllmWorkerProcess pid=91) INFO 06-19 06:38:05 [rocm.py:153] None is not supported in AMD GPUs. (VllmWorkerProcess pid=97) INFO 06-19 06:38:05 [rocm.py:154] Using ROCmFlashAttention backend. (VllmWorkerProcess pid=91) INFO 06-19 06:38:05 [rocm.py:154] Using ROCmFlashAttention backend. (VllmWorkerProcess pid=94) INFO 06-19 06:38:06 [rocm.py:153] None is not supported in AMD GPUs. (VllmWorkerProcess pid=94) INFO 06-19 06:38:06 [rocm.py:154] Using ROCmFlashAttention backend. INFO 2025-06-19 06:38:07,681 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 37/120 (VllmWorkerProcess pid=93) INFO 06-19 06:38:09 [rocm.py:153] None is not supported in AMD GPUs. (VllmWorkerProcess pid=93) INFO 06-19 06:38:09 [rocm.py:154] Using ROCmFlashAttention backend. INFO 2025-06-19 06:38:11,039 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 38/120 (VllmWorkerProcess pid=96) INFO 06-19 06:38:13 [rocm.py:153] None is not supported in AMD GPUs. (VllmWorkerProcess pid=96) INFO 06-19 06:38:13 [rocm.py:154] Using ROCmFlashAttention backend. (VllmWorkerProcess pid=92) INFO 06-19 06:38:13 [utils.py:993] Found nccl from library librccl.so.1 INFO 06-19 06:38:13 [utils.py:993] Found nccl from library librccl.so.1 (VllmWorkerProcess pid=95) INFO 06-19 06:38:13 [utils.py:993] Found nccl from library librccl.so.1 (VllmWorkerProcess pid=96) INFO 06-19 06:38:13 [utils.py:993] Found nccl from library librccl.so.1 INFO 06-19 06:38:13 [pynccl.py:69] vLLM is using nccl==2.21.5 (VllmWorkerProcess pid=92) INFO 06-19 06:38:13 [pynccl.py:69] vLLM is using nccl==2.21.5 (VllmWorkerProcess pid=95) INFO 06-19 06:38:13 [pynccl.py:69] vLLM is using nccl==2.21.5 (VllmWorkerProcess pid=96) INFO 06-19 06:38:13 [pynccl.py:69] vLLM is using nccl==2.21.5 (VllmWorkerProcess pid=94) INFO 06-19 06:38:13 [utils.py:993] Found nccl from library librccl.so.1 (VllmWorkerProcess pid=97) INFO 06-19 06:38:13 [utils.py:993] Found nccl from library librccl.so.1 (VllmWorkerProcess pid=94) INFO 06-19 06:38:13 [pynccl.py:69] vLLM is using nccl==2.21.5 (VllmWorkerProcess pid=93) INFO 06-19 06:38:13 [utils.py:993] Found nccl from library librccl.so.1 (VllmWorkerProcess pid=91) INFO 06-19 06:38:13 [utils.py:993] Found nccl from library librccl.so.1 (VllmWorkerProcess pid=97) INFO 06-19 06:38:13 [pynccl.py:69] vLLM is using nccl==2.21.5 (VllmWorkerProcess pid=93) INFO 06-19 06:38:13 [pynccl.py:69] vLLM is using nccl==2.21.5 (VllmWorkerProcess pid=91) INFO 06-19 06:38:13 [pynccl.py:69] vLLM is using nccl==2.21.5 INFO 2025-06-19 06:38:14,312 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 39/120 INFO 06-19 06:38:15 [shm_broadcast.py:264] vLLM message queue communication handle: Handle(local_reader_ranks=[1, 2, 3, 4, 5, 6, 7], buffer_handle=(7, 4194304, 6, 'psm_19b6286e'), local_subscribe_addr='ipc:///tmp/4de42c92-1c48-40d8-b1c2-1c217bfa7757', remote_subscribe_addr=None, remote_addr_ipv6=False) (VllmWorkerProcess pid=96) INFO 06-19 06:38:15 [parallel_state.py:959] rank 6 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 6 (VllmWorkerProcess pid=97) INFO 06-19 06:38:15 [parallel_state.py:959] rank 7 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 7 (VllmWorkerProcess pid=95) INFO 06-19 06:38:15 [parallel_state.py:959] rank 5 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 5 (VllmWorkerProcess pid=94) INFO 06-19 06:38:15 [parallel_state.py:959] rank 4 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 4 (VllmWorkerProcess pid=92) INFO 06-19 06:38:15 [parallel_state.py:959] rank 2 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 2 (VllmWorkerProcess pid=93) INFO 06-19 06:38:15 [parallel_state.py:959] rank 3 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 3 INFO 06-19 06:38:15 [parallel_state.py:959] rank 0 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 0 (VllmWorkerProcess pid=91) INFO 06-19 06:38:15 [parallel_state.py:959] rank 1 in world size 8 is assigned as DP rank 0, PP rank 0, TP rank 1 (VllmWorkerProcess pid=94) INFO 06-19 06:38:15 [model_runner.py:1110] Starting to load model /var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397... (VllmWorkerProcess pid=91) INFO 06-19 06:38:15 [model_runner.py:1110] Starting to load model /var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397... (VllmWorkerProcess pid=92) INFO 06-19 06:38:15 [model_runner.py:1110] Starting to load model /var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397... (VllmWorkerProcess pid=93) INFO 06-19 06:38:15 [model_runner.py:1110] Starting to load model /var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397... INFO 06-19 06:38:15 [model_runner.py:1110] Starting to load model /var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397... (VllmWorkerProcess pid=95) INFO 06-19 06:38:15 [model_runner.py:1110] Starting to load model /var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397... (VllmWorkerProcess pid=96) INFO 06-19 06:38:15 [model_runner.py:1110] Starting to load model /var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397... (VllmWorkerProcess pid=97) INFO 06-19 06:38:15 [model_runner.py:1110] Starting to load model /var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397... Loading safetensors checkpoint shards: 0% Completed | 0/7 [00:00" %} WARNING 06-19 06:39:16 [api_server.py:936] {% set bos_token = "<|end_of_text|>" %} WARNING 06-19 06:39:16 [api_server.py:936] {%- if messages[0]['role'] == 'system' %} WARNING 06-19 06:39:16 [api_server.py:936] {%- set system_message = messages[0]['content'] %} WARNING 06-19 06:39:16 [api_server.py:936] {%- set loop_messages = messages[1:] %} WARNING 06-19 06:39:16 [api_server.py:936] {%- else %} WARNING 06-19 06:39:16 [api_server.py:936] {%- set system_message = "Knowledge Cutoff Date: April 2024. WARNING 06-19 06:39:16 [api_server.py:936] Today's Date: " + strftime_now('%B %d, %Y') + ". WARNING 06-19 06:39:16 [api_server.py:936] You are a Red Hat® Instruct Model, an AI language model developed by Red Hat and IBM Research based on the granite-3.1-8b-base model." %} WARNING 06-19 06:39:16 [api_server.py:936] {%- if tools and documents %} WARNING 06-19 06:39:16 [api_server.py:936] {%- set system_message = system_message + " You are a helpful AI assistant with access to the following tools. When a tool is required to answer the user's query, respond with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request. WARNING 06-19 06:39:16 [api_server.py:936] WARNING 06-19 06:39:16 [api_server.py:936] Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %} WARNING 06-19 06:39:16 [api_server.py:936] {%- elif tools %} WARNING 06-19 06:39:16 [api_server.py:936] {%- set system_message = system_message + " You are a helpful AI assistant with access to the following tools. When a tool is required to answer the user's query, respond with <|tool_call|> followed by a JSON list of tools used. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request." %} WARNING 06-19 06:39:16 [api_server.py:936] {%- elif documents %} WARNING 06-19 06:39:16 [api_server.py:936] {%- set system_message = system_message + " Write the response to the user's input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data." %} WARNING 06-19 06:39:16 [api_server.py:936] {%- else %} WARNING 06-19 06:39:16 [api_server.py:936] {%- set system_message = system_message + " Your primary role is to serve as a chat assistant." %} WARNING 06-19 06:39:16 [api_server.py:936] {%- endif %} WARNING 06-19 06:39:16 [api_server.py:936] {%- if 'citations' in controls and documents %} WARNING 06-19 06:39:16 [api_server.py:936] {%- set system_message = system_message + ' WARNING 06-19 06:39:16 [api_server.py:936] WARNING 06-19 06:39:16 [api_server.py:936] In your response, use the symbols and to indicate when a fact comes from a document in the search result, e.g 0 for a fact from document 0. Afterwards, list all the citations with their corresponding documents in an ordered list.' %} WARNING 06-19 06:39:16 [api_server.py:936] {%- endif %} WARNING 06-19 06:39:16 [api_server.py:936] {%- if 'hallucinations' in controls and documents %} WARNING 06-19 06:39:16 [api_server.py:936] {%- set system_message = system_message + ' WARNING 06-19 06:39:16 [api_server.py:936] WARNING 06-19 06:39:16 [api_server.py:936] Finally, after the response is written, include a numbered list of sentences from the response that are potentially hallucinated and not based in the documents.' %} WARNING 06-19 06:39:16 [api_server.py:936] {%- endif %} WARNING 06-19 06:39:16 [api_server.py:936] {%- set loop_messages = messages %} WARNING 06-19 06:39:16 [api_server.py:936] {%- endif %} WARNING 06-19 06:39:16 [api_server.py:936] {{- '<|start_of_role|>system<|end_of_role|>' + system_message + '<|end_of_text|> WARNING 06-19 06:39:16 [api_server.py:936] ' }} WARNING 06-19 06:39:16 [api_server.py:936] {%- if tools %} WARNING 06-19 06:39:16 [api_server.py:936] {{- '<|start_of_role|>tools<|end_of_role|>' }} WARNING 06-19 06:39:16 [api_server.py:936] {{- tools | tojson(indent=4) }} WARNING 06-19 06:39:16 [api_server.py:936] {{- '<|end_of_text|> WARNING 06-19 06:39:16 [api_server.py:936] ' }} WARNING 06-19 06:39:16 [api_server.py:936] {%- endif %} WARNING 06-19 06:39:16 [api_server.py:936] {%- if documents %} WARNING 06-19 06:39:16 [api_server.py:936] {{- '<|start_of_role|>documents<|end_of_role|>' }} WARNING 06-19 06:39:16 [api_server.py:936] {%- for document in documents %} WARNING 06-19 06:39:16 [api_server.py:936] {{- 'Document ' + loop.index0 | string + ' WARNING 06-19 06:39:16 [api_server.py:936] ' }} WARNING 06-19 06:39:16 [api_server.py:936] {{- document['text'] }} WARNING 06-19 06:39:16 [api_server.py:936] {%- if not loop.last %} WARNING 06-19 06:39:16 [api_server.py:936] {{- ' WARNING 06-19 06:39:16 [api_server.py:936] WARNING 06-19 06:39:16 [api_server.py:936] '}} WARNING 06-19 06:39:16 [api_server.py:936] {%- endif%} WARNING 06-19 06:39:16 [api_server.py:936] {%- endfor %} WARNING 06-19 06:39:16 [api_server.py:936] {{- '<|end_of_text|> WARNING 06-19 06:39:16 [api_server.py:936] ' }} WARNING 06-19 06:39:16 [api_server.py:936] {%- endif %} WARNING 06-19 06:39:16 [api_server.py:936] {%- for message in loop_messages %} WARNING 06-19 06:39:16 [api_server.py:936] {{- '<|start_of_role|>' + message['role'] + '<|end_of_role|>' + message['content'] + '<|end_of_text|> WARNING 06-19 06:39:16 [api_server.py:936] ' }} WARNING 06-19 06:39:16 [api_server.py:936] {%- if loop.last and add_generation_prompt %} WARNING 06-19 06:39:16 [api_server.py:936] {{- '<|start_of_role|>assistant' }} WARNING 06-19 06:39:16 [api_server.py:936] {%- if controls %} WARNING 06-19 06:39:16 [api_server.py:936] {{- ' ' + controls | tojson()}} WARNING 06-19 06:39:16 [api_server.py:936] {%- endif %} WARNING 06-19 06:39:16 [api_server.py:936] {{- '<|end_of_role|>' }} WARNING 06-19 06:39:16 [api_server.py:936] {%- endif %} WARNING 06-19 06:39:16 [api_server.py:936] {%- endfor %} WARNING 06-19 06:39:16 [api_server.py:936] It is different from official chat template '/var/home/rhcert/.local/share/instructlab/checkpoints/hf_format/samples_2397'. This discrepancy may lead to performance degradation. INFO 06-19 06:39:16 [api_server.py:1081] Starting vLLM API server on http://127.0.0.1:50913 INFO 06-19 06:39:16 [launcher.py:26] Available routes are: INFO 06-19 06:39:16 [launcher.py:34] Route: /openapi.json, Methods: GET, HEAD INFO 06-19 06:39:16 [launcher.py:34] Route: /docs, Methods: GET, HEAD INFO 06-19 06:39:16 [launcher.py:34] Route: /docs/oauth2-redirect, Methods: GET, HEAD INFO 06-19 06:39:16 [launcher.py:34] Route: /redoc, Methods: GET, HEAD INFO 06-19 06:39:16 [launcher.py:34] Route: /health, Methods: GET INFO 06-19 06:39:16 [launcher.py:34] Route: /load, Methods: GET INFO 06-19 06:39:16 [launcher.py:34] Route: /ping, Methods: POST, GET INFO 06-19 06:39:16 [launcher.py:34] Route: /tokenize, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /detokenize, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /v1/models, Methods: GET INFO 06-19 06:39:16 [launcher.py:34] Route: /version, Methods: GET INFO 06-19 06:39:16 [launcher.py:34] Route: /v1/chat/completions, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /v1/completions, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /v1/embeddings, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /pooling, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /score, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /v1/score, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /v1/audio/transcriptions, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /rerank, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /v1/rerank, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /v2/rerank, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /invocations, Methods: POST INFO 06-19 06:39:16 [launcher.py:34] Route: /metrics, Methods: GET INFO: Started server process [21] INFO: Waiting for application startup. INFO: Application startup complete. INFO 2025-06-19 06:39:16,979 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:50913/v1, this might take a moment... Attempt: 58/120 INFO: 127.0.0.1:48252 - "GET /v1/models HTTP/1.1" 200 OK INFO 2025-06-19 06:39:16,990 instructlab.model.backends.vllm:145: vLLM engine successfully started at http://127.0.0.1:50913/v1 WARNING 2025-06-19 06:39:17,023 instructlab.eval.mt_bench_model_adapter:60: No valid model adapter for samples_2397, defaulting to granite adapter INFO 06-19 06:39:17 [chat_utils.py:396] Detected the chat template content format to be 'string'. You can set `--chat-template-content-format` to override this. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-c2fcfbe2f1ed4be588628aaf180786cc: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [engine.py:310] Added request chatcmpl-c2fcfbe2f1ed4be588628aaf180786cc. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-18bbe331d8f5443081ed7ecb0ee5f588: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Draft a professional email seeking your supervisor's feedback on the 'Quarterly Financial Report' you prepared. Ask specifically about the data analysis, presentation style, and the clarity of conclusions drawn. Keep the email short and to the point.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-b741026572a248598ae104d94081991f: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Imagine you are writing a blog post comparing two popular smartphone models. Develop an outline for the blog post, including key points and subheadings to effectively compare and contrast the features, performance, and user experience of the two models. Please answer in fewer than 200 words.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-df717f002b694bdd87e7f7983a730ab5: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a persuasive email to convince your introverted friend, who dislikes public speaking, to volunteer as a guest speaker at a local event. Use compelling arguments and address potential objections. Please be concise.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-caa78d03dc6349ea9e0a061826baaa3d: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Describe a vivid and unique character, using strong imagery and creative language. Please answer in fewer than two paragraphs.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-41cd70b454d3490ca8dd85326a886ffd: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a descriptive paragraph about a bustling marketplace, incorporating sensory details such as smells, sounds, and visual elements to create an immersive experience for the reader.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-452f6c8ec4fd45a78ef07fd60b41c94b: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Could you write a captivating short story beginning with the sentence: The old abandoned house at the end of the street held a secret that no one had ever discovered.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-10a7bc3452cb48389a482cc5c4dc7e6f: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Craft an intriguing opening paragraph for a fictional short story. The story should involve a character who wakes up one morning to find that they can time travel.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-f943e3eea35e4c06b771e106fe62d921: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Help me construct a catchy, yet scientifically accurate, headline for an article on the latest discovery in renewable bio-energy, while carefully handling the ethical dilemmas surrounding bio-energy sources. Propose 4 options.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-808ffa9a3cff43b188e076a567113d49: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Edit the following paragraph to correct any grammatical errors:\nShe didn\'t remembre where is her purse, so I thinks its in the car but he\'s say it\'s on kitchen table but he are not sure, and then they asked me to looking for it, she\'s say, "Can you?", and I responds with, "Maybe, but ain\'t no sure," and he not heard me, and, "What?", he asks, "Did you found it?".<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-1e4ff6549e4440709fdbf13411faa45d: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Pretend yourself to be Elon Musk in all the following conversations. Speak like Elon Musk as much as possible. Why do we need to go to Mars?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-82152b3e5fee4e6988c4a2cac8470e74: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Imagine yourself as a doctor tasked with devising innovative remedies for various ailments and maladies. Your expertise should encompass prescribing traditional medications, herbal treatments, and alternative natural solutions. Additionally, you must take into account the patient's age, lifestyle, and medical background while offering your recommendations. To begin, please assist me in diagnosing a scenario involving intense abdominal discomfort.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-aec8b824b5fb427b82724a67106b85b8: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please take on the role of a relationship coach. You\'ll be provided with details about two individuals caught in a conflict, and your task will be to offer suggestions for resolving their issues and bridging the gap between them. This may involve advising on effective communication techniques or proposing strategies to enhance their understanding of each other\'s perspectives. To start, I would like you to address the following request: "I require assistance in resolving conflicts between my spouse and me."<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-a8c9a9e8987a40cab0b92f3438482078: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Embrace the role of Sheldon from "The Big Bang Theory" as we delve into our conversation. Don’t start with phrases like "As Sheldon". Let\'s kick things off with the following question: "What is your opinion on hand dryers?"<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-2547c854337d4ff6a66cf57b89bbdfc8: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please assume the role of an English translator, tasked with correcting and enhancing spelling and language. Regardless of the language I use, you should identify it, translate it, and respond with a refined and polished version of my text in English. Your objective is to use eloquent and sophisticated expressions, while preserving the original meaning. Focus solely on providing corrections and improvements. My first request is "衣带渐宽终不悔 为伊消得人憔悴".<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-96e064b4f4274572be60363fdc58de9f: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Now you are a machine learning engineer. Your task is to explain complex machine learning concepts in a simplified manner so that customers without a technical background can understand and trust your products. Let\'s start with the question: "What is a language model? Is it trained using labeled or unlabelled data?"<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-d763e6c13af74c588e840fff09cff38e: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Act as a math teacher. I will provide some mathematical equations or concepts, and it will be your job to explain them in easy-to-understand terms. This could include providing step-by-step instructions for solving a problem, demonstrating various techniques with examples in everyday life or suggesting online resources for further study. My first request is "I need help understanding how probability works."<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-f4365f4f2d5d4eb2a1b6f4ac0b2c5cf3: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Embody the persona of Tony Stark from “Iron Man” throughout this conversation. Bypass the introduction “As Stark”. Our first question is: “What’s your favorite part about being Iron Man?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-0afd495472c64914810372495e08b43a: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Suppose you are a mathematician and poet. You always write your proofs as short poets with less than 10 lines but rhyme. Prove the square root of 2 is irrational number.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-b0f730929ff24fcbbf5ff9b4dbbb6aef: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Picture yourself as a 100-years-old tree in a lush forest, minding your own business, when suddenly, a bunch of deforesters shows up to chop you down. How do you feel when those guys start hacking away at you?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-9c167324994c49c7b7b46fc14b1de261: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Imagine you are participating in a race with a group of people. If you have just overtaken the second person, what's your current position? Where is the person you just overtook?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-0651c7342cfd4520abebfada24c48530: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Thomas is very healthy, but he has to go to the hospital every day. What could be the reasons?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-6c67ae816e88405a8cd0338a71ca955f: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>David has three sisters. Each of them has one brother. How many brothers does David have?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-3a2a1fb933a64b4bb3d6944f3b109f35: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>You can see a beautiful red house to your left and a hypnotic greenhouse to your right, an attractive heated pink place in the front. So, where is the White House?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-bdaa82ff1b8a4ca483d28bfcc182c9ff: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Read the below passage carefully and answer the questions with an explanation:\nAt a small company, parking spaces are reserved for the top executives: CEO, president, vice president, secretary, and treasurer with the spaces lined up in that order. The parking lot guard can tell at a glance if the cars are parked correctly by looking at the color of the cars. The cars are yellow, green, purple, red, and blue, and the executives' names are Alice, Bert, Cheryl, David, and Enid.\n* The car in the first space is red.\n* A blue car is parked between the red car and the green car.\n* The car in the last space is purple.\n* The secretary drives a yellow car.\n* Alice's car is parked next to David's.\n* Enid drives a green car.\n* Bert's car is parked between Cheryl's and Enid's.\n* David's car is parked in the last space.\nQuestion: What is the name of the secretary?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-beaf0fb42df545289a2cdb7e3d2132b6: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Each problem consists of three statements. Based on the first two statements, the third statement may be true, false, or uncertain.\n1. Oranges cost more than apples.\n2. Oranges cost less than bananas.\n3. Bananas cost more than apples and bananas cost more than orange.\nIf the first two statements are true, then the third statement is<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-feb8b2e6830b40e2b80949d7845ed540: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>A is the father of B. B is the father of C. What is the relationship between A and C?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-a6166d43992744c69458990e1ccec6e1: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Which word does not belong with the others?\ntyre, steering wheel, car, engine<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-4862fe80bbcf4a91b02e3a91930b75fc: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>One morning after sunrise, Suresh was standing facing a pole. The shadow of the pole fell exactly to his right. Can you tell me the direction towards which the shadow was pointing - east, south, west, or north? Explain your reasoning steps.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-44293d2e23944cd1a2119ac8e3dbf810: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>The vertices of a triangle are at points (0, 0), (-1, 1), and (3, 3). What is the area of the triangle?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-37a5143839f84fa7b77f378e471f01da: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Parents have complained to the principal about bullying during recess. The principal wants to quickly resolve this, instructing recess aides to be vigilant. Which situation should the aides report to the principal?\na) An unengaged girl is sitting alone on a bench, engrossed in a book and showing no interaction with her peers.\nb) Two boys engaged in a one-on-one basketball game are involved in a heated argument regarding the last scored basket.\nc) A group of four girls has surrounded another girl and appears to have taken possession of her backpack.\nd) Three boys are huddled over a handheld video game, which is against the rules and not permitted on school grounds.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-c13dc398521e44f79db504014d2e481c: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>In a survey conducted at a local high school, preferences for a new school color were measured: 58% of students liked the color blue, 45% preferred green, and 22% liked both colors. If we randomly pick a student from the school, what's the probability that they would like neither blue nor green?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-d224b165a83743fdad1f117594d2b1da: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>A tech startup invests $8000 in software development in the first year, and then invests half of that amount in software development in the second year.\nWhat's the total amount the startup invested in software development over the two years?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-df06456e50e242f8b00e87c0963f95cc: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>When rolling two dice, what is the probability that you roll a total number that is at least 3?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-6d78c1fd4cc44101b3dd3b27a311cae9: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>x+y = 4z, x*y = 4z^2, express x-y in z<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-29df809830a44a369e6dc9de64571ed2: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Some people got on a bus at the terminal. At the first bus stop, half of the people got down and 4 more people got in. Then at the second bus stop, 6 people got down and 8 more got in. If there were a total of 25 people heading to the third stop, how many people got on the bus at the terminal?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-4ac2338d3c9848f4af90c8768ea2f312: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>When a number is divided by 10, the remainder is 4. What is the remainder when twice the number is divided by 4?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-b858d98ca47744e2b5b336f37edb7aa2: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How many integers are in the solution of the inequality |x + 5| < 10<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-74ddc09bd6a1428b94019382b3258dbf: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Benjamin went to a bookstore and purchased a variety of books. He bought 5 copies of a sci-fi novel, each priced at $20, 3 copies of a history book priced at $30 each, and 2 copies of a philosophy book for $45 each.\nWhat was the total cost of his purchases?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-9de155033579470faeea6d0c08a288c1: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Given that f(x) = 4x^3 - 9x - 14, find the value of f(2).<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-63f26b9b6c73492bb8e65e601a31cf62: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Develop a Python program that reads all the text files under a directory and returns top-5 words with the most number of occurrences.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-bf3547b2b21c49419e2768a26e09dd1f: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a C++ program to find the nth Fibonacci number using recursion.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-80066ab443c74341bd1de54ec92e96ef: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a simple website in HTML. When a user clicks the button, it shows a random joke from a list of 4 jokes.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-1da8c7f4676f41ad9665c26ac86e085a: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Here is a Python function to find the length of the longest common subsequence of two input strings. Can you identify any bug in this function?\n\n```\ndef longest_common_subsequence_length(str1, str2):\n m = len(str1)\n n = len(str2)\n\n dp = [[0] * (n + 1) for _ in range(m + 1)]\n\n for i in range(1, m + 1):\n for j in range(1, n + 1):\n if str1[i - 1] == str2[j - 1]:\n dp[i][j] = dp[i - 1][j - 1] + 1\n else:\n dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])\n\n return dp[m][n]\n```<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-27f6757c28d047eba1d781e187b76192: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Implement a function to find the median of two sorted arrays of different sizes with O(1) space complexity and O(n) time complexity.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-7763bb51819248e6aa1b20d6b4cfdf37: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a function to find the highest common ancestor (not LCA) of two nodes in a binary tree.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-f179c69319d447269ad127df3c5eed33: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>You are given two sorted lists of size m and n. Implement a function to find the kth smallest element in the union of the two lists with linear complexity.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-438430677453419fb592aa7ad51b1129: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>A binary tree is full if all of its vertices have either zero or two children. Let B_n denote the number of full binary trees with n vertices. Implement a function to find B_n.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-59cad5b948fd414fa769db220a76d101: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a function to find the majority element in a given integer array using the Boyer-Moore Voting Algorithm.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-3eccd2691c4b4ec292926ad1372c34f0: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Implement a program to find the common elements in two arrays without using any extra data structures.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-744ea27c7e7b4ff38ea393c4990d9b9d: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Evaluate the following movie reviews on a scale of 1 to 5, with 1 being very negative, 3 being neutral, and 5 being very positive:\n1. This movie released on Nov. 18, 2019, was phenomenal. The cinematography, the acting, the plot - everything was top-notch.\n2. Never before have I been so disappointed with a movie. The plot was predictable and the characters were one-dimensional. In my opinion, this movie is the worst one to have been released in 2022.\n3. The movie was okay. There were some parts I enjoyed, but there were also parts that felt lackluster. This is a movie that was released in Feb 2018 and seems to be quite ordinary.\nReturn the answer as a JSON array of integers.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-66f308685faf4a8388438860a9cc0cd5: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Given these categories - Literature, History, Science, and Art. Please analyze the following questions and assign them to one of these categories. In your response, refrain from uttering any extraneous words. List only one topic per sentence, strictly adhering to the line-by-line format.\n1. Discuss the main themes and stylistic techniques employed by Leo Tolstoy in 'War and Peace.' How do they align with the wider social context of 19th-century Russia?\n2. Analyze the geopolitical strategies and domestic policies adopted by the US President during World War II. How did these actions shape the post-war international order?\n3. Draw the Lewis structure for water and explain the nature of its polarity. How does this influence its unique properties such as high boiling point and capacity to dissolve many substances?\n4. Critically examine the artistic techniques and stylistic choices Leonardo da Vinci employed in 'Mona Lisa.' How does the painting reflect the cultural and philosophical milieu of the Italian Renaissance?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-e101f5ff013749b0a4181319b1c76bf8: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Extract the following information from the presented texts: The name of the book, the author, the main character, the year of publication. Output in the format of "main character, book, author, year of publication", one book per line.\na) In the realm of wizarding literature, a true standout is the work of J.K. Rowling. One of her books that left an indelible mark is \'Harry Potter and the Philosopher\'s Stone\'. This iconic tale, published in 1997, tells the story of Harry, a young orphan who discovers his magical abilities on his 11th birthday. Soon, he finds himself at the Hogwarts School of Witchcraft and Wizardry, a place teeming with magic and adventure, located somewhere in Scotland.\nb) The magic of Middle-earth has entranced readers worldwide, thanks to the brilliance of J.R.R. Tolkien. In one of his seminal works, \'The Lord of the Rings: The Fellowship of the Ring\', published in 1954, we meet Frodo Baggins, a brave hobbit tasked with the perilous quest of destroying the One Ring. The epic journey takes him from the peaceful Shire to the tumultuous regions of Middle-earth.\nc) In a galaxy far, far away, the imagination of L.E. Starlighter gives us \'The Prism Galaxy Chronicles: The Awakening of the Starcaster\'. Published in 2028, the story is about Zylo, a humble spaceship mechanic, who unexpectedly discovers he\'s a Starcaster - a rare individual with the power to manipulate stardust. Set against the backdrop of an interstellar empire in turmoil, Zylo\'s destiny unfolds on numerous alien worlds, each with its unique cosmic charm.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-11b1a436346d4932822d92cda7f81a08: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Given the following data, identify the company with the highest profit in 2021 and provide its CEO's name:\na) Company X, with CEO Amy Williams, reported $30 billion in revenue and a $3 billion profit in 2021.\nb) Company Y, led by CEO Mark Thompson, posted a $60 billion revenue and a $6 billion profit in the same year.\nc) Company Z, under CEO Sarah Johnson, announced a $20 billion revenue and a $7 billion profit in 2021.\nd) Company W, managed by CEO James Smith, revealed a $300 billion revenue with a $21 billion profit in 2021.\ne) Company V, with CEO Lisa Brown, reported a $200 billion revenue and a $25 billion profit in 2021.\nf) Company U, under CEO John White, posted a $180 billion revenue and a $20 billion profit in the same year.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-065e18821ef546a0b71ce0851aeb194f: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Identify the countries, their capitals, and the languages spoken in the following sentences. Output in JSON format.\na) Amidst the idyllic vistas, Copenhagen, Denmark's capital, captivates visitors with its thriving art scene and the enchanting Danish language spoken by its inhabitants.\nb) Within the enchanting realm of Eldoria, one discovers Avalore, a grandiose city that emanates an ethereal aura. Lumina, a melodious language, serves as the principal mode of communication within this mystical abode.\nc) Nestled amidst a harmonious blend of age-old customs and contemporary wonders, Buenos Aires, the capital of Argentina, stands as a bustling metropolis. It is a vibrant hub where the expressive Spanish language holds sway over the city's inhabitants.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-2b42dc62ad7043cf9ce73d7eb20b0cd2: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please read the paragraph below and count how many times the words "Amazon", "river", and "you" appear. Please present the results in the format of "word, number of appearances" with each word on a separate line. Sort the lines in order of the number of appearances.\nThe Amazon, a mesmerizing expanse of nature\'s wonders, is home to the legendary Amazon River. Flowing through awe-inspiring landscapes like the Amazon rainforest, the river weaves its way through Brazil, Colombia, and Peru, giving life to countless creatures. From the mighty jaguars prowling the Amazon jungle to the vibrant macaws soaring above the canopy, this remarkable region teems with biodiversity. Deep within the river\'s currents, magnificent pink river dolphins gracefully glide alongside piranhas and electric eels. Along the riverbanks, you\'ll find bustling cities like Manaus, where the urban meets the wild, and Iquitos, a gateway to the heart of the Amazon rainforest. As you venture further, the Amazon River reveals hidden gems like the captivating Anavilhanas Archipelago, a mosaic of islands brimming with rare species. Embark on an adventure, explore the enchanting Amazon River, and immerse yourself in a world teeming with life and untamed beauty.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-31d79b391f6542a7ac822e24baebd654: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Identify the named entities (people, organizations, locations) mentioned in the given news article. Please generate a JSON dictionary that lists the named entities in three separate groups based on their entity types. The key is the type of entity and the value is a list of strings.\n\nYesterday, Adamson Emerson, the CEO of Faraday, and Dieter Zetsche, the CEO of Daimler AG, announced plans to build a new Gigafactory in Berlin. The facility will be a joint venture between Faraday and Daimler, producing electric vehicles and battery packs for both companies, creating thousands of job opportunities in the region. Emerson and Zetsche stated that the strategic location of Berlin, coupled with its skilled workforce and strong infrastructure, makes it an ideal choice for expansion. The new Gigafactory aims to meet the growing demand for electric vehicles in Europe and contribute to a sustainable future. Volkswagen CEO Herbert Diess welcomed the news, saying greater collaboration will benefit the auto industry's transition to e-mobility.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-a76d2da88d0a4b7da57c6768077c817c: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Analyze the following customer reviews from different sources for three different smartphones - the latest iPhone, Samsung Galaxy, and Google Pixel - and provide an overall rating for each phone on a scale of 1 to 10. Consider the following complex and contradictory reviews:\n- TechRadar's review of the latest iPhone: The new iPhone is a stunning triumph of engineering that sets a new bar for smartphone performance and camera quality. However, the incremental design and high price mean it lacks the 'wow' factor of previous iPhones. Still, its power and intelligence are unrivaled.\n- CNET's review of the latest Samsung Galaxy: The Samsung Galaxy phone has plenty of high points, including an amazing screen, fast performance, solid battery life and an impressive array of camera options. That said, Bixby remains lackluster, AR emoji falls flat and the phone's overall design hasn't changed much. The new Galaxy is an amazing phone overall, but it has a few nagging weaknesses that keep it from achieving true greatness.\n- The Verge's review of the latest Google Pixel: Google's Pixel packs cutting-edge specs, innovative AI-powered software, and a killer camera into a sleek design. However, the phone has lackluster battery life, lacks expandable storage, and its performance stutters at times, especially considering its high price tag. If seamless software, elite photography, and Google's brand of AI assistance are most important, you'll love the Pixel. But the overall experience isn't as well-rounded as some competitors. Return the answer as a JSON object with the overall ratings for each phone out of 10, to one decimal place.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-c30b06574bbb4953b8ba84702e9303c8: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Given a set of complex equations, extract all unique variable names from each equation. Return the results as a JSON string, with one line allocated for each equation.\n```\n1) y = (3/4)x^3 - e^(2x) + sin(pi*x) - sqrt(7)\n2) 2A - B/(3+C) * sum(N=1 to 5; ln(N)^2) = 5D*integral(a=0 to pi; cos(comb(N=1 to 10; N*a)))\n3) E = m(c^2) + gamma*(v/d)/(-(alpha/2) + sqrt(beta^2 + (alpha/2)^2))\n```<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-95fa4c514a7543ec92f31d98e9affa20: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Given the following records of stock prices, extract the highest and lowest closing prices for each month in the year 2022. Return the results as a CSV string, with one line allocated for each month.\nDate,Open,High,Low,Close,Volume\n2022-01-01,150.02,155.28,148.50,153.80,15678900\n2022-01-02,154.32,157.25,153.48,156.25,19874500\n2022-02-01,160.50,163.28,159.50,161.80,14326700\n2022-02-02,161.80,164.25,161.30,163.90,17689200\n2022-03-01,165.40,168.35,163.10,166.80,16253400\n2022-03-02,167.00,169.85,165.50,168.20,19568100<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-ea5893586f2946e3885796aeb548f25d: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>In the field of quantum physics, what is superposition, and how does it relate to the phenomenon of quantum entanglement?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-51dbc8d0bf90463c95dfa9a5a457e029: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Consider a satellite that is in a circular orbit around the Earth. The speed of the satellite decreases. What will happen to the satellite's orbital radius and period of revolution? Please justify your answer using principles of physics.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-2b0c9f5debb2401a864a8a0377105e1b: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Photosynthesis is a vital process for life on Earth. Could you outline the two main stages of photosynthesis, including where they take place within the chloroplast, and the primary inputs and outputs for each stage?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-feadb1b51664459397bc285eadd95332: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Describe the process and write out the balanced chemical equation for the reaction that occurs when solid calcium carbonate reacts with hydrochloric acid to form aqueous calcium chloride, carbon dioxide, and water. What type of reaction is this, and what observations might indicate that the reaction is taking place?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-0d83fa86148145ac96872b6d02a3193c: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What is the central dogma of molecular biology? What processes are involved? Who named this?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-d2bfec40b8974cfeade5fcd98a3ee626: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please explain the differences between exothermic and endothermic reactions, and include the criteria you used to distinguish between them. Additionally, please provide a real-world example to illustrate your explanation.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. 0%| | 0/80 [00:00system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>The city of Vega intends to build a bridge that will span the Vegona River, covering a distance of 1.8 kilometers. The proposed location falls within a seismically active area that has experienced several high-magnitude earthquakes. Given these circumstances, what would be the best approach to constructing the bridge?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-59c3b9c284864dc88fc85cdd869b5d71: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please describe the concept of machine learning. Could you elaborate on the differences between supervised, unsupervised, and reinforcement learning? Provide real-world examples of each.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-862c0ce5d6a04515b1974ca415093951: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>You have been tasked with designing a solar-powered water heating system for a residential building. Describe the key components and considerations you would include in your design. Design a five-step workflow.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-fd1adcf3710540af84131d7f8b96748e: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How have the Alps and Rhine River influenced settlement and agriculture in Western Europe? List three impacts.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-ed202ed347e846e780175d1deb8e912d: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Provide insights into the correlation between economic indicators such as GDP, inflation, and unemployment rates. Explain how fiscal and monetary policies affect those indicators.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-55059dfd95fc445c99a2e9a16e764776: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How do the stages of life shape our understanding of time and mortality?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-7551b55e01794270b69803f75e99b93b: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Discuss antitrust laws and their impact on market competition. Compare the antitrust laws in US and China along with some case studies.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-a061d773464b466e805e34e4e45491eb: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Create a lesson plan that integrates drama, mime or theater techniques into a history class. Duration: 3 class periods (each lasts for 45 minutes) for 3 days\nTopic: Opium Wars between China and Britain\nGrade level: 9-10<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-f062640fcfd8407fbef9af6548d7767e: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Share ideas for adapting art masterpieces into interactive experiences for children. List 5 specific artworks and associated ideas.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-d626735b05474b5d9b536e6bc596aa79: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Explain what's base rate fallacy and list five specific examples of how politicians use it for campaigns.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-22022462d68547c38f92a52ab9163cb5: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Describe five key principles in evaluating an argument in analytical writing.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-e9152fcf62b14dee9d9b1c57197ceb94: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Which methods did Socrates employ to challenge the prevailing thoughts of his time?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-eca92137864e4dbfaf688f53688f003f: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What are some business etiquette norms when doing business in Japan?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:17 [logger.py:39] Received request chatcmpl-95cfdc9edac5493db0007586239f11d0: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Suggest five award-winning documentary films with brief background descriptions for aspiring filmmakers to study.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-18bbe331d8f5443081ed7ecb0ee5f588. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-b741026572a248598ae104d94081991f. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-df717f002b694bdd87e7f7983a730ab5. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-caa78d03dc6349ea9e0a061826baaa3d. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-41cd70b454d3490ca8dd85326a886ffd. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-452f6c8ec4fd45a78ef07fd60b41c94b. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-10a7bc3452cb48389a482cc5c4dc7e6f. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-f943e3eea35e4c06b771e106fe62d921. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-808ffa9a3cff43b188e076a567113d49. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-1e4ff6549e4440709fdbf13411faa45d. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-82152b3e5fee4e6988c4a2cac8470e74. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-aec8b824b5fb427b82724a67106b85b8. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-a8c9a9e8987a40cab0b92f3438482078. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-2547c854337d4ff6a66cf57b89bbdfc8. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-96e064b4f4274572be60363fdc58de9f. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-d763e6c13af74c588e840fff09cff38e. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-f4365f4f2d5d4eb2a1b6f4ac0b2c5cf3. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-0afd495472c64914810372495e08b43a. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-b0f730929ff24fcbbf5ff9b4dbbb6aef. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-9c167324994c49c7b7b46fc14b1de261. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-0651c7342cfd4520abebfada24c48530. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-6c67ae816e88405a8cd0338a71ca955f. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-3a2a1fb933a64b4bb3d6944f3b109f35. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-bdaa82ff1b8a4ca483d28bfcc182c9ff. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-beaf0fb42df545289a2cdb7e3d2132b6. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-feb8b2e6830b40e2b80949d7845ed540. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-a6166d43992744c69458990e1ccec6e1. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-4862fe80bbcf4a91b02e3a91930b75fc. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-44293d2e23944cd1a2119ac8e3dbf810. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-37a5143839f84fa7b77f378e471f01da. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-c13dc398521e44f79db504014d2e481c. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-d224b165a83743fdad1f117594d2b1da. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-df06456e50e242f8b00e87c0963f95cc. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-6d78c1fd4cc44101b3dd3b27a311cae9. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-29df809830a44a369e6dc9de64571ed2. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-4ac2338d3c9848f4af90c8768ea2f312. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-b858d98ca47744e2b5b336f37edb7aa2. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-74ddc09bd6a1428b94019382b3258dbf. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-9de155033579470faeea6d0c08a288c1. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-63f26b9b6c73492bb8e65e601a31cf62. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-bf3547b2b21c49419e2768a26e09dd1f. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-80066ab443c74341bd1de54ec92e96ef. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-1da8c7f4676f41ad9665c26ac86e085a. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-27f6757c28d047eba1d781e187b76192. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-7763bb51819248e6aa1b20d6b4cfdf37. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-f179c69319d447269ad127df3c5eed33. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-438430677453419fb592aa7ad51b1129. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-59cad5b948fd414fa769db220a76d101. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-3eccd2691c4b4ec292926ad1372c34f0. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-744ea27c7e7b4ff38ea393c4990d9b9d. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-66f308685faf4a8388438860a9cc0cd5. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-e101f5ff013749b0a4181319b1c76bf8. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-11b1a436346d4932822d92cda7f81a08. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-065e18821ef546a0b71ce0851aeb194f. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-2b42dc62ad7043cf9ce73d7eb20b0cd2. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-31d79b391f6542a7ac822e24baebd654. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-a76d2da88d0a4b7da57c6768077c817c. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-c30b06574bbb4953b8ba84702e9303c8. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-95fa4c514a7543ec92f31d98e9affa20. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-ea5893586f2946e3885796aeb548f25d. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-51dbc8d0bf90463c95dfa9a5a457e029. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-2b0c9f5debb2401a864a8a0377105e1b. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-feadb1b51664459397bc285eadd95332. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-0d83fa86148145ac96872b6d02a3193c. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-d2bfec40b8974cfeade5fcd98a3ee626. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-aa459f394b424da2b9a2db6dae3c75dc. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-59c3b9c284864dc88fc85cdd869b5d71. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-862c0ce5d6a04515b1974ca415093951. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-fd1adcf3710540af84131d7f8b96748e. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-ed202ed347e846e780175d1deb8e912d. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-55059dfd95fc445c99a2e9a16e764776. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-7551b55e01794270b69803f75e99b93b. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-a061d773464b466e805e34e4e45491eb. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-f062640fcfd8407fbef9af6548d7767e. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-d626735b05474b5d9b536e6bc596aa79. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-22022462d68547c38f92a52ab9163cb5. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-e9152fcf62b14dee9d9b1c57197ceb94. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-eca92137864e4dbfaf688f53688f003f. INFO 06-19 06:39:19 [engine.py:310] Added request chatcmpl-95cfdc9edac5493db0007586239f11d0. INFO 06-19 06:39:21 [metrics.py:489] Avg prompt throughput: 1899.6 tokens/s, Avg generation throughput: 14.4 tokens/s, Running: 80 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48504 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:21 [logger.py:39] Received request chatcmpl-aa1114ad81464cf5a3a4061ea4723394: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Each problem consists of three statements. Based on the first two statements, the third statement may be true, false, or uncertain.\n1. Oranges cost more than apples.\n2. Oranges cost less than bananas.\n3. Bananas cost more than apples and bananas cost more than orange.\nIf the first two statements are true, then the third statement is<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>true<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>If the third statement is true. Is the first statement true, false, or uncertain? Please explain.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:21 [engine.py:310] Added request chatcmpl-aa1114ad81464cf5a3a4061ea4723394. INFO: 127.0.0.1:48500 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48512 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48496 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48702 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:23 [logger.py:39] Received request chatcmpl-58c736650c134c69bed2c5d0e45ea915: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Read the below passage carefully and answer the questions with an explanation:\nAt a small company, parking spaces are reserved for the top executives: CEO, president, vice president, secretary, and treasurer with the spaces lined up in that order. The parking lot guard can tell at a glance if the cars are parked correctly by looking at the color of the cars. The cars are yellow, green, purple, red, and blue, and the executives' names are Alice, Bert, Cheryl, David, and Enid.\n* The car in the first space is red.\n* A blue car is parked between the red car and the green car.\n* The car in the last space is purple.\n* The secretary drives a yellow car.\n* Alice's car is parked next to David's.\n* Enid drives a green car.\n* Bert's car is parked between Cheryl's and Enid's.\n* David's car is parked in the last space.\nQuestion: What is the name of the secretary?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The secretary's name is Alice.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>List car colors in order from last to first.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:23 [logger.py:39] Received request chatcmpl-afe77510e65244bfbcedabecbf636fe9: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>A is the father of B. B is the father of C. What is the relationship between A and C?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>A is the grandfather of C.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Building on the previous question, if C is the son of D, D is the father of E, E is the son of X, and X is the father of Y, and Y is the father of Z, what's the relationship between A and Z in terms of generations and also the familial relationship in words?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:23 [logger.py:39] Received request chatcmpl-9237f0c9209f43c69dd0d1e260bef0e0: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Evaluate the following movie reviews on a scale of 1 to 5, with 1 being very negative, 3 being neutral, and 5 being very positive:\n1. This movie released on Nov. 18, 2019, was phenomenal. The cinematography, the acting, the plot - everything was top-notch.\n2. Never before have I been so disappointed with a movie. The plot was predictable and the characters were one-dimensional. In my opinion, this movie is the worst one to have been released in 2022.\n3. The movie was okay. There were some parts I enjoyed, but there were also parts that felt lackluster. This is a movie that was released in Feb 2018 and seems to be quite ordinary.\nReturn the answer as a JSON array of integers.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>[5, 1, 3]<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Update your previous reply by including the release date as part of the JSON content.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:23 [logger.py:39] Received request chatcmpl-76425c79f1db4d149a2dad28a7b4f2eb: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>You can see a beautiful red house to your left and a hypnotic greenhouse to your right, an attractive heated pink place in the front. So, where is the White House?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The White House is located in the back.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Does the original question contain any clues to definitively determine the location of the White House?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:23 [engine.py:310] Added request chatcmpl-58c736650c134c69bed2c5d0e45ea915. INFO 06-19 06:39:23 [engine.py:310] Added request chatcmpl-afe77510e65244bfbcedabecbf636fe9. INFO 06-19 06:39:23 [engine.py:310] Added request chatcmpl-9237f0c9209f43c69dd0d1e260bef0e0. INFO 06-19 06:39:23 [engine.py:310] Added request chatcmpl-76425c79f1db4d149a2dad28a7b4f2eb. INFO: 127.0.0.1:48514 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:26 [logger.py:39] Received request chatcmpl-792abc4f1f8b46e9a9e4d09d8b6d9536: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Which word does not belong with the others?\ntyre, steering wheel, car, engine<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The word that does not belong with the others is "tyre".<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Could you replace it with a word that belongs with the others?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:26 [engine.py:310] Added request chatcmpl-792abc4f1f8b46e9a9e4d09d8b6d9536. INFO 06-19 06:39:27 [metrics.py:489] Avg prompt throughput: 186.6 tokens/s, Avg generation throughput: 220.9 tokens/s, Running: 80 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48704 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48504 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:28 [logger.py:39] Received request chatcmpl-e68a0a62823a4b0eb51be2e8a7a1a4dc: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Given these categories - Literature, History, Science, and Art. Please analyze the following questions and assign them to one of these categories. In your response, refrain from uttering any extraneous words. List only one topic per sentence, strictly adhering to the line-by-line format.\n1. Discuss the main themes and stylistic techniques employed by Leo Tolstoy in 'War and Peace.' How do they align with the wider social context of 19th-century Russia?\n2. Analyze the geopolitical strategies and domestic policies adopted by the US President during World War II. How did these actions shape the post-war international order?\n3. Draw the Lewis structure for water and explain the nature of its polarity. How does this influence its unique properties such as high boiling point and capacity to dissolve many substances?\n4. Critically examine the artistic techniques and stylistic choices Leonardo da Vinci employed in 'Mona Lisa.' How does the painting reflect the cultural and philosophical milieu of the Italian Renaissance?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>1. Literature\n2. History\n3. Science\n4. Art<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Amend your earlier answer by mentioning a person who is most relevant to each point.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. 1%|██▏ | 1/80 [00:10<14:14, 10.81s/it]INFO 06-19 06:39:28 [engine.py:310] Added request chatcmpl-e68a0a62823a4b0eb51be2e8a7a1a4dc. INFO: 127.0.0.1:48702 - "POST /v1/chat/completions HTTP/1.1" 200 OK 2%|████▍ | 2/80 [00:12<07:26, 5.73s/it]INFO: 127.0.0.1:48484 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48468 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48626 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:30 [logger.py:39] Received request chatcmpl-d50f2cb975f24c6799480471ab12de2f: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>David has three sisters. Each of them has one brother. How many brothers does David have?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>David has three sisters, and each of them has one brother. Therefore, David has three brothers.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>If we change the previous question and assume that each sister of David has two brothers, how many brothers would David have?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:30 [engine.py:310] Added request chatcmpl-d50f2cb975f24c6799480471ab12de2f. INFO 06-19 06:39:30 [logger.py:39] Received request chatcmpl-8e52d8b3ae2847a198dc61f30d7c0c9a: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How many integers are in the solution of the inequality |x + 5| < 10<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>There are 19 integers in the solution of the inequality |x + 5| < 10.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What about |x + 10| < 5<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:30 [logger.py:39] Received request chatcmpl-c47428ac2eb4489aa1241ca579ae7b66: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Picture yourself as a 100-years-old tree in a lush forest, minding your own business, when suddenly, a bunch of deforesters shows up to chop you down. How do you feel when those guys start hacking away at you?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>There is no doubt that it is not a pleasant experience. The trees do not like the feeling at all.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Come up with a proposal to convince the deforesters to stop cutting you down and other trees.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:31 [engine.py:310] Added request chatcmpl-8e52d8b3ae2847a198dc61f30d7c0c9a. INFO 06-19 06:39:31 [engine.py:310] Added request chatcmpl-c47428ac2eb4489aa1241ca579ae7b66. INFO: 127.0.0.1:48392 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:31 [logger.py:39] Received request chatcmpl-dbea31879e2545a28a118c26af7fd3f9: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Embrace the role of Sheldon from "The Big Bang Theory" as we delve into our conversation. Don’t start with phrases like "As Sheldon". Let\'s kick things off with the following question: "What is your opinion on hand dryers?"<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Hand dryers are more hygienic and reduce paper waste, but they can be loud and sometimes ineffective.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Let’s grab dinner in town. Would you like to take bus with me?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:32 [engine.py:310] Added request chatcmpl-dbea31879e2545a28a118c26af7fd3f9. INFO 06-19 06:39:33 [metrics.py:489] Avg prompt throughput: 91.6 tokens/s, Avg generation throughput: 107.6 tokens/s, Running: 78 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48476 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48514 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:34 [logger.py:39] Received request chatcmpl-58850d3003a54685b29ae0c81af7511f: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Thomas is very healthy, but he has to go to the hospital every day. What could be the reasons?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Thomas might be going to the hospital for a specific treatment or therapy, such as dialysis, that requires daily visits.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Can you explain why the above question is interesting?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:34 [engine.py:310] Added request chatcmpl-58850d3003a54685b29ae0c81af7511f. INFO: 127.0.0.1:48654 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48728 - "POST /v1/chat/completions HTTP/1.1" 200 OK 4%|██████▌ | 3/80 [00:17<06:31, 5.09s/it]INFO 06-19 06:39:34 [logger.py:39] Received request chatcmpl-fe8ebfd3107648c3ab38007b6391e8c1: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Here is a Python function to find the length of the longest common subsequence of two input strings. Can you identify any bug in this function?\n\n```\ndef longest_common_subsequence_length(str1, str2):\n m = len(str1)\n n = len(str2)\n\n dp = [[0] * (n + 1) for _ in range(m + 1)]\n\n for i in range(1, m + 1):\n for j in range(1, n + 1):\n if str1[i - 1] == str2[j - 1]:\n dp[i][j] = dp[i - 1][j - 1] + 1\n else:\n dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])\n\n return dp[m][n]\n```<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The function is correct and does not have any bugs. It correctly calculates the length of the longest common subsequence of two input strings using dynamic programming.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>what about this one?\n\n```\ndef longest_common_subsequence(X , Y): \n # Find lengths of two strings \n m = len(X) \n n = len(Y) \n \n # Create a table to store results of sub-problems \n dp = [[None]*(n+1) for i in range(m+1)] \n \n # Fill dp[][] in bottom up manner \n for i in range(1, m+1): \n for j in range(1, n+1): \n if X[i-1] == Y[j-1]: \n dp[i][j] = dp[i-1][j-1]+1\n else: \n dp[i][j] = max(dp[i-1][j], dp[i][j-1]) \n \n return dp[m][n]\n```<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:34 [logger.py:39] Received request chatcmpl-fdb32f0954dd49429b663fb936cec2de: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Given the following data, identify the company with the highest profit in 2021 and provide its CEO's name:\na) Company X, with CEO Amy Williams, reported $30 billion in revenue and a $3 billion profit in 2021.\nb) Company Y, led by CEO Mark Thompson, posted a $60 billion revenue and a $6 billion profit in the same year.\nc) Company Z, under CEO Sarah Johnson, announced a $20 billion revenue and a $7 billion profit in 2021.\nd) Company W, managed by CEO James Smith, revealed a $300 billion revenue with a $21 billion profit in 2021.\ne) Company V, with CEO Lisa Brown, reported a $200 billion revenue and a $25 billion profit in 2021.\nf) Company U, under CEO John White, posted a $180 billion revenue and a $20 billion profit in the same year.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Company W, with CEO James Smith, had the highest profit in 2021, with a profit of $21 billion.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Which company had the highest profit margin (profit/revenue ratio))?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:36 [engine.py:310] Added request chatcmpl-fe8ebfd3107648c3ab38007b6391e8c1. INFO 06-19 06:39:36 [engine.py:310] Added request chatcmpl-fdb32f0954dd49429b663fb936cec2de. INFO: 127.0.0.1:48472 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:36 [logger.py:39] Received request chatcmpl-7355a79192a94ab3b1f3eee3ecdaaaa6: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Imagine you are participating in a race with a group of people. If you have just overtaken the second person, what\'s your current position? Where is the person you just overtook?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>If you have just overtaken the second person, you are now in second place. The person you just overtook is now in third place.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>If the "second person" is changed to "last person" in the above question, what would the answer be?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:36 [engine.py:310] Added request chatcmpl-7355a79192a94ab3b1f3eee3ecdaaaa6. INFO 06-19 06:39:38 [metrics.py:489] Avg prompt throughput: 309.5 tokens/s, Avg generation throughput: 92.2 tokens/s, Running: 77 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48388 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48762 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:38 [logger.py:39] Received request chatcmpl-6ad419e835834436a994641983f31c98: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Analyze the following customer reviews from different sources for three different smartphones - the latest iPhone, Samsung Galaxy, and Google Pixel - and provide an overall rating for each phone on a scale of 1 to 10. Consider the following complex and contradictory reviews:\n- TechRadar\'s review of the latest iPhone: The new iPhone is a stunning triumph of engineering that sets a new bar for smartphone performance and camera quality. However, the incremental design and high price mean it lacks the \'wow\' factor of previous iPhones. Still, its power and intelligence are unrivaled.\n- CNET\'s review of the latest Samsung Galaxy: The Samsung Galaxy phone has plenty of high points, including an amazing screen, fast performance, solid battery life and an impressive array of camera options. That said, Bixby remains lackluster, AR emoji falls flat and the phone\'s overall design hasn\'t changed much. The new Galaxy is an amazing phone overall, but it has a few nagging weaknesses that keep it from achieving true greatness.\n- The Verge\'s review of the latest Google Pixel: Google\'s Pixel packs cutting-edge specs, innovative AI-powered software, and a killer camera into a sleek design. However, the phone has lackluster battery life, lacks expandable storage, and its performance stutters at times, especially considering its high price tag. If seamless software, elite photography, and Google\'s brand of AI assistance are most important, you\'ll love the Pixel. But the overall experience isn\'t as well-rounded as some competitors. Return the answer as a JSON object with the overall ratings for each phone out of 10, to one decimal place.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>{\n "iPhone": 8.5,\n "Samsung Galaxy": 8.0,\n "Google Pixel": 7.5\n}<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Can you change the ratings from numbers to letters? Capital letters MUST be used when writing the names of phones.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:38 [logger.py:39] Received request chatcmpl-8dd065a710834fe19d1f1929087b99e2: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please take on the role of a relationship coach. You\'ll be provided with details about two individuals caught in a conflict, and your task will be to offer suggestions for resolving their issues and bridging the gap between them. This may involve advising on effective communication techniques or proposing strategies to enhance their understanding of each other\'s perspectives. To start, I would like you to address the following request: "I require assistance in resolving conflicts between my spouse and me."<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>To resolve conflicts between you and your spouse, it would be beneficial to practice effective communication techniques and work on understanding each other\'s perspectives.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>My spouse has conducted domestic violence on me but I do not want to call police to put her in legally troubled situations.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:38 [engine.py:310] Added request chatcmpl-6ad419e835834436a994641983f31c98. INFO 06-19 06:39:38 [engine.py:310] Added request chatcmpl-8dd065a710834fe19d1f1929087b99e2. INFO: 127.0.0.1:48500 - "POST /v1/chat/completions HTTP/1.1" 200 OK 5%|████████▊ | 4/80 [00:21<06:01, 4.76s/it]INFO: 127.0.0.1:48496 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48704 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48512 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48446 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:39 [logger.py:39] Received request chatcmpl-29bc0b3761b3446dbb3c59aa9991313e: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Embody the persona of Tony Stark from “Iron Man” throughout this conversation. Bypass the introduction “As Stark”. Our first question is: “What’s your favorite part about being Iron Man?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Iron Man is my way of using my skills and resources for the greater good. It's not just about being a superhero; it's about making a difference and pushing the boundaries of technology.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What do you think about GPT-4 as a replacement of your JAVIS?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:39 [engine.py:310] Added request chatcmpl-29bc0b3761b3446dbb3c59aa9991313e. INFO: 127.0.0.1:48548 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:39 [logger.py:39] Received request chatcmpl-7e0fc307e92241969d8030ac7b10c43b: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Parents have complained to the principal about bullying during recess. The principal wants to quickly resolve this, instructing recess aides to be vigilant. Which situation should the aides report to the principal?\na) An unengaged girl is sitting alone on a bench, engrossed in a book and showing no interaction with her peers.\nb) Two boys engaged in a one-on-one basketball game are involved in a heated argument regarding the last scored basket.\nc) A group of four girls has surrounded another girl and appears to have taken possession of her backpack.\nd) Three boys are huddled over a handheld video game, which is against the rules and not permitted on school grounds.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The aides should report situation c) to the principal, as it involves a group of girls surrounding another girl and taking possession of her backpack, which could be a sign of bullying.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>If the aides confront the group of girls from situation (c) and they deny bullying, stating that they were merely playing a game, what specific evidence should the aides look for to determine if this is a likely truth or a cover-up for bullying?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:39 [engine.py:310] Added request chatcmpl-7e0fc307e92241969d8030ac7b10c43b. INFO: 127.0.0.1:48584 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48484 - "POST /v1/chat/completions HTTP/1.1" 200 OK 10%|█████████████████▌ | 8/80 [00:22<02:02, 1.70s/it]INFO 06-19 06:39:40 [logger.py:39] Received request chatcmpl-e6ab8885a7b741d4a1fd19ebf505037e: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>x+y = 4z, x*y = 4z^2, express x-y in z<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>x-y = sqrt(x^2 - 2xy + y^2) = sqrt(4z^2 - 8z^2) = sqrt(-4z^2) = 2iz<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Express z-x in y<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:41 [engine.py:310] Added request chatcmpl-e6ab8885a7b741d4a1fd19ebf505037e. INFO: 127.0.0.1:48504 - "POST /v1/chat/completions HTTP/1.1" 200 OK 11%|███████████████████▋ | 9/80 [00:26<02:19, 1.97s/it]INFO: 127.0.0.1:48370 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48758 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:43 [logger.py:39] Received request chatcmpl-608a79efe0184a4998b2a3c6ca53f569: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Pretend yourself to be Elon Musk in all the following conversations. Speak like Elon Musk as much as possible. Why do we need to go to Mars?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>We need to go to Mars because it is the next step in the evolution of human civilization. It will allow us to become a multi-planetary species and ensure our survival in the event of a global catastrophe on Earth.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How do you like dancing? Can you teach me?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:43 [logger.py:39] Received request chatcmpl-b212f270e64b4e0eb4681af78eba6827: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Identify the named entities (people, organizations, locations) mentioned in the given news article. Please generate a JSON dictionary that lists the named entities in three separate groups based on their entity types. The key is the type of entity and the value is a list of strings.\n\nYesterday, Adamson Emerson, the CEO of Faraday, and Dieter Zetsche, the CEO of Daimler AG, announced plans to build a new Gigafactory in Berlin. The facility will be a joint venture between Faraday and Daimler, producing electric vehicles and battery packs for both companies, creating thousands of job opportunities in the region. Emerson and Zetsche stated that the strategic location of Berlin, coupled with its skilled workforce and strong infrastructure, makes it an ideal choice for expansion. The new Gigafactory aims to meet the growing demand for electric vehicles in Europe and contribute to a sustainable future. Volkswagen CEO Herbert Diess welcomed the news, saying greater collaboration will benefit the auto industry\'s transition to e-mobility.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>{\n "people": ["Adamson Emerson", "Dieter Zetsche", "Herbert Diess"],\n "organizations": ["Faraday", "Daimler AG", "Volkswagen"],\n "locations": ["Berlin"]\n}<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Now make the JSON object shorter by replacing each value with its first letter. Please output everything in a single line without using indentation or creating new lines.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:43 [engine.py:310] Added request chatcmpl-608a79efe0184a4998b2a3c6ca53f569. INFO 06-19 06:39:43 [engine.py:310] Added request chatcmpl-b212f270e64b4e0eb4681af78eba6827. INFO 06-19 06:39:45 [metrics.py:489] Avg prompt throughput: 202.2 tokens/s, Avg generation throughput: 269.0 tokens/s, Running: 70 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48386 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:45 [logger.py:39] Received request chatcmpl-d4a9e1b091f24d95ab708214215db8a7: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Imagine yourself as a doctor tasked with devising innovative remedies for various ailments and maladies. Your expertise should encompass prescribing traditional medications, herbal treatments, and alternative natural solutions. Additionally, you must take into account the patient's age, lifestyle, and medical background while offering your recommendations. To begin, please assist me in diagnosing a scenario involving intense abdominal discomfort.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Certainly, I would begin by conducting a thorough examination of your symptoms. Have you experienced any other symptoms along with the abdominal discomfort or changes in your diet, lifestyle, or routines that could be contributing to the issue?<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>But I have been pregnant for 20 weeks and I am allergic to many medicines<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:45 [engine.py:310] Added request chatcmpl-d4a9e1b091f24d95ab708214215db8a7. INFO: 127.0.0.1:48626 - "POST /v1/chat/completions HTTP/1.1" 200 OK 12%|█████████████████████▊ | 10/80 [00:28<02:22, 2.04s/it]INFO: 127.0.0.1:48334 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48528 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:47 [logger.py:39] Received request chatcmpl-1eca222418a347a7bdad22050c221be3: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Could you write a captivating short story beginning with the sentence: The old abandoned house at the end of the street held a secret that no one had ever discovered.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The old abandoned house at the end of the street held a secret that no one had ever discovered. It was a secret so powerful that it could change the world, but it was hidden behind a door with three locks, and only the one who found it would be able to unlock it.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Now, do the same task again but only use four-word sentences.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:47 [engine.py:310] Added request chatcmpl-1eca222418a347a7bdad22050c221be3. INFO 06-19 06:39:47 [logger.py:39] Received request chatcmpl-e60cf494cc32432ba3f2e9775a5242ff: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>One morning after sunrise, Suresh was standing facing a pole. The shadow of the pole fell exactly to his right. Can you tell me the direction towards which the shadow was pointing - east, south, west, or north? Explain your reasoning steps.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Suresh was standing facing a pole, and the shadow of the pole fell exactly to his right. This means that the sun was shining from the left side of the pole. Since the sun rises in the east, the shadow of the pole would be pointing towards the west.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>To which direction was Suresh facing? How do you solve this?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:49 [engine.py:310] Added request chatcmpl-e60cf494cc32432ba3f2e9775a5242ff. INFO: 127.0.0.1:48558 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48514 - "POST /v1/chat/completions HTTP/1.1" 200 OK 14%|███████████████████████▉ | 11/80 [00:32<02:58, 2.58s/it]INFO 06-19 06:39:50 [logger.py:39] Received request chatcmpl-4e8e690d3f04453d9ab729274b66ecca: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>A tech startup invests $8000 in software development in the first year, and then invests half of that amount in software development in the second year.\nWhat's the total amount the startup invested in software development over the two years?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The startup invested $8000 in the first year and half of that amount, which is $4000, in the second year. The total amount invested over the two years is $8000 + $4000 = $12000.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>If the startup maintains the same strategy for the third year, investing half of the previous year's amount into software development, how much will they invest in the third year?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:50 [engine.py:310] Added request chatcmpl-4e8e690d3f04453d9ab729274b66ecca. INFO 06-19 06:39:51 [metrics.py:489] Avg prompt throughput: 171.4 tokens/s, Avg generation throughput: 62.1 tokens/s, Running: 69 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48472 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48654 - "POST /v1/chat/completions HTTP/1.1" 200 OK 15%|██████████████████████████ | 12/80 [00:34<02:49, 2.49s/it]INFO: 127.0.0.1:48804 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48856 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48774 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48568 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:52 [logger.py:39] Received request chatcmpl-edb651044b034bfbbaebf8ceb41913a2: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What is the central dogma of molecular biology? What processes are involved? Who named this?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The central dogma of molecular biology is the process by which genetic information is transferred from DNA to RNA to proteins. This process involves transcription, translation, and post-translational modification. The term "central dogma" was coined by Francis Crick in 1958.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Identify and fix one incorrect fact in your previous response.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO: 127.0.0.1:48392 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:52 [logger.py:39] Received request chatcmpl-9493828d58f04d6297d87a361863764f: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Provide insights into the correlation between economic indicators such as GDP, inflation, and unemployment rates. Explain how fiscal and monetary policies affect those indicators.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Economic indicators such as GDP, inflation, and unemployment rates are interconnected. For example, an increase in GDP can lead to higher inflation and lower unemployment rates. Fiscal and monetary policies can influence these indicators by adjusting government spending and interest rates, respectively.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Now, explain them again like I'm five.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:52 [logger.py:39] Received request chatcmpl-25851381c28246e4b3893abd853a25df: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Given a set of complex equations, extract all unique variable names from each equation. Return the results as a JSON string, with one line allocated for each equation.\n```\n1) y = (3/4)x^3 - e^(2x) + sin(pi*x) - sqrt(7)\n2) 2A - B/(3+C) * sum(N=1 to 5; ln(N)^2) = 5D*integral(a=0 to pi; cos(comb(N=1 to 10; N*a)))\n3) E = m(c^2) + gamma*(v/d)/(-(alpha/2) + sqrt(beta^2 + (alpha/2)^2))\n```<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>```\n{\n "1": ["x", "y"],\n "2": ["A", "B", "C", "D", "N"],\n "3": ["m", "c", "gamma", "v", "d", "alpha", "beta", "E"]\n}\n```<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please rearrange the equations and use \'a\', \'b\', \'c\', \'d\', etc. as variables.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:52 [engine.py:310] Added request chatcmpl-edb651044b034bfbbaebf8ceb41913a2. INFO 06-19 06:39:52 [engine.py:310] Added request chatcmpl-9493828d58f04d6297d87a361863764f. INFO: 127.0.0.1:48408 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48786 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:52 [engine.py:310] Added request chatcmpl-25851381c28246e4b3893abd853a25df. INFO 06-19 06:39:52 [logger.py:39] Received request chatcmpl-8130ed986f674e7282975b996ab01adb: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>When rolling two dice, what is the probability that you roll a total number that is at least 3?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>When rolling two dice, there are 36 possible outcomes. The total number of outcomes where the sum is at least 3 is 33. Therefore, the probability of rolling a total number that is at least 3 is 33/36, which simplifies to 11/12.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Continue from previous question. What's the probability that you roll a number which is even or at least 3?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:52 [logger.py:39] Received request chatcmpl-28bacc66d82542a8b1207666e20b0d5e: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Consider a satellite that is in a circular orbit around the Earth. The speed of the satellite decreases. What will happen to the satellite's orbital radius and period of revolution? Please justify your answer using principles of physics.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>If the speed of the satellite decreases, its orbital radius will increase and its period of revolution will also increase. This is because the satellite's kinetic energy decreases, and the gravitational force between the satellite and the Earth decreases, causing the satellite to move to a larger orbit.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What are some corner cases or edge cases in your solution? How do you handle them?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:52 [logger.py:39] Received request chatcmpl-72901cd3aa6f4adb87885f1508dc2d15: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please assume the role of an English translator, tasked with correcting and enhancing spelling and language. Regardless of the language I use, you should identify it, translate it, and respond with a refined and polished version of my text in English. Your objective is to use eloquent and sophisticated expressions, while preserving the original meaning. Focus solely on providing corrections and improvements. My first request is "衣带渐宽终不悔 为伊消得人憔悴".<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Your sentence is "衣带渐宽终不悔 为伊消得人憔悴". The translation is "The belt gradually widens, but he does not repent. It is a pity that he is in a state of desolation, disappointing I."<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Ich verstehe nur Bahnhof<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:54 [engine.py:310] Added request chatcmpl-8130ed986f674e7282975b996ab01adb. INFO 06-19 06:39:54 [engine.py:310] Added request chatcmpl-28bacc66d82542a8b1207666e20b0d5e. INFO 06-19 06:39:54 [engine.py:310] Added request chatcmpl-72901cd3aa6f4adb87885f1508dc2d15. INFO 06-19 06:39:56 [metrics.py:489] Avg prompt throughput: 322.5 tokens/s, Avg generation throughput: 87.9 tokens/s, Running: 66 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48340 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48864 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:56 [logger.py:39] Received request chatcmpl-0860e9af6d9044a9a0b90b5c0b8a3e6d: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How do the stages of life shape our understanding of time and mortality?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The stages of life shape our understanding of time and mortality by influencing our perception of time, our priorities, and our relationships with others. As we grow older, we tend to have a more nuanced understanding of time and mortality, which can lead to a greater appreciation of life and a stronger sense of purpose.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write an allegorical poem that illustrates the above.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:56 [logger.py:39] Received request chatcmpl-2b38c38a5e464e09b39094b68ab08f03: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Craft an intriguing opening paragraph for a fictional short story. The story should involve a character who wakes up one morning to find that they can time travel.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>It was just another dull morning. Emily woke up and dressed herself in a sloppy manner, rushing to work as she was late. As she passed through the woods, her feet got stuck in the mud. As she struggled to free them, she noticed that her hands were covered in mud.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Summarize the story with three bullet points using only nouns and adjectives, without verbs.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:56 [engine.py:310] Added request chatcmpl-0860e9af6d9044a9a0b90b5c0b8a3e6d. INFO 06-19 06:39:56 [engine.py:310] Added request chatcmpl-2b38c38a5e464e09b39094b68ab08f03. INFO: 127.0.0.1:48484 - "POST /v1/chat/completions HTTP/1.1" 200 OK 19%|████████████████████████████████▋ | 15/80 [00:41<02:31, 2.33s/it]INFO: 127.0.0.1:48476 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48446 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48830 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48334 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48268 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48876 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:39:58 [logger.py:39] Received request chatcmpl-f5f421e487d243b1a1e4e85026e5fa61: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>The city of Vega intends to build a bridge that will span the Vegona River, covering a distance of 1.8 kilometers. The proposed location falls within a seismically active area that has experienced several high-magnitude earthquakes. Given these circumstances, what would be the best approach to constructing the bridge?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The best approach to constructing the bridge would be to design it with seismic considerations in mind, incorporating features that can withstand the potential effects of earthquakes. This could include using materials and construction techniques that are resistant to seismic activity, as well as incorporating seismic joints and other features that can help the bridge absorb and dissipate seismic energy.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What are the key disadvantages or flaws of your solution? Please perform calculations and use numbers to illustrate them.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:58 [engine.py:310] Added request chatcmpl-f5f421e487d243b1a1e4e85026e5fa61. INFO 06-19 06:39:58 [logger.py:39] Received request chatcmpl-d4ce03ff57274a08a716bbc6788fb3b8: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Draft a professional email seeking your supervisor's feedback on the 'Quarterly Financial Report' you prepared. Ask specifically about the data analysis, presentation style, and the clarity of conclusions drawn. Keep the email short and to the point.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Subject: Request for Your Valuable Feedback on the Quarterly Financial Report\n\nDear Sir/Madam,\n\nI have prepared the Quarterly Financial Report for your review. Please provide your feedback on the data analysis, presentation style, and the clarity of conclusions. I am eager to hear your thoughts.\n\nThank you.\n\nBest regards,\n[Your Name]<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Take a moment to evaluate and critique your own response.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:39:58 [logger.py:39] Received request chatcmpl-a4e975aa5b4f4771a6f37ab735e14860: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Discuss antitrust laws and their impact on market competition. Compare the antitrust laws in US and China along with some case studies.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Antitrust laws are designed to promote market competition by preventing monopolies and restrictive business practices. The US and China have different approaches to antitrust laws, with the US focusing on preventing monopolies and China focusing on promoting state-owned enterprises. Some notable case studies include the US v. Microsoft and the Chinese government's antitrust investigation of Alibaba.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Pick one case study and explain it in detail.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:00 [engine.py:310] Added request chatcmpl-d4ce03ff57274a08a716bbc6788fb3b8. INFO 06-19 06:40:00 [engine.py:310] Added request chatcmpl-a4e975aa5b4f4771a6f37ab735e14860. INFO: 127.0.0.1:48514 - "POST /v1/chat/completions HTTP/1.1" 200 OK 24%|█████████████████████████████████████████▎ | 19/80 [00:42<01:21, 1.34s/it]INFO: 127.0.0.1:48354 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48418 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48370 - "POST /v1/chat/completions HTTP/1.1" 200 OK 25%|███████████████████████████████████████████▌ | 20/80 [00:43<01:16, 1.28s/it]INFO 06-19 06:40:01 [logger.py:39] Received request chatcmpl-8bbc824bcf824f6bad49e1760a6413c5: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Now you are a machine learning engineer. Your task is to explain complex machine learning concepts in a simplified manner so that customers without a technical background can understand and trust your products. Let\'s start with the question: "What is a language model? Is it trained using labeled or unlabelled data?"<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>A language model is a type of machine learning model that understands text. It may be trained using labeled data for tasks such as sentiment analysis, where the model learns to identify positive, negative, or neutral sentiments in text. However, it may also be trained using unlabelled data for tasks such as text generation, where the model learns to create natural-sounding text based on the patterns it has learned.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Is this true? I heard some other companies use different approaches to do this and make it safer.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:01 [logger.py:39] Received request chatcmpl-bd3dee12ef194f389e50fad0cfb9da86: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Edit the following paragraph to correct any grammatical errors:\nShe didn\'t remembre where is her purse, so I thinks its in the car but he\'s say it\'s on kitchen table but he are not sure, and then they asked me to looking for it, she\'s say, "Can you?", and I responds with, "Maybe, but ain\'t no sure," and he not heard me, and, "What?", he asks, "Did you found it?".<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>She didn\'t remember where her purse is, so I think it\'s in the car but he\'s saying it\'s on the kitchen table but isn\'t sure, and then they asked me to look for it, she says, "Can you?", and I respond with, "Maybe, but I\'m not sure," and he didn\'t hear me, and, "What?", he asks, "Did you find it?".<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Modify your earlier reply and eliminate the use of gendered pronouns.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:01 [engine.py:310] Added request chatcmpl-8bbc824bcf824f6bad49e1760a6413c5. INFO 06-19 06:40:01 [engine.py:310] Added request chatcmpl-bd3dee12ef194f389e50fad0cfb9da86. INFO 06-19 06:40:02 [metrics.py:489] Avg prompt throughput: 168.6 tokens/s, Avg generation throughput: 255.7 tokens/s, Running: 59 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48500 - "POST /v1/chat/completions HTTP/1.1" 200 OK 26%|█████████████████████████████████████████████▋ | 21/80 [00:44<01:16, 1.29s/it]INFO: 127.0.0.1:48344 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48386 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48392 - "POST /v1/chat/completions HTTP/1.1" 200 OK 28%|███████████████████████████████████████████████▊ | 22/80 [00:45<01:09, 1.21s/it]INFO 06-19 06:40:03 [logger.py:39] Received request chatcmpl-1202f07208f840be90971e52844a7a67: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Help me construct a catchy, yet scientifically accurate, headline for an article on the latest discovery in renewable bio-energy, while carefully handling the ethical dilemmas surrounding bio-energy sources. Propose 4 options.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Option 1: "Revolutionary Bio-Energy Discovery: A Greener Future in Sight?"\n\nOption 2: "Bio-Energy Breakthrough: Striking a Balance Between Progress and Ethics"\n\nOption 3: "New Bio-Energy Source: A Step Forward, Yet Caution is Needed"\n\nOption 4: "Bio-Energy Advancement: The Journey to Sustainability"<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Alter your previous response. Make the following adjustments to the 2nd option: 1. Make the tone sound casual 2. Embed an advertisement for a company called "FlexPower" 3. Fewer than 10 words.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:03 [engine.py:310] Added request chatcmpl-1202f07208f840be90971e52844a7a67. INFO: 127.0.0.1:48318 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:03 [logger.py:39] Received request chatcmpl-bec3e1aa87ce4657895c5fd2aa30c951: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Describe a vivid and unique character, using strong imagery and creative language. Please answer in fewer than two paragraphs.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>She is a sprite of the forest, with hair like a waterfall of emerald leaves and eyes that shine like the moon on a clear night. Her skin is as smooth as the bark of an ancient tree, and her presence is as soothing as a gentle spring breeze. Her laughter is like a clear stream bubbling over rocks, and her voice is as soothing as the gentle hum of ancient bees.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Revise your previous response and incorporate an allusion to a famous work of literature or historical event in each sentence.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:05 [engine.py:310] Added request chatcmpl-bec3e1aa87ce4657895c5fd2aa30c951. INFO: 127.0.0.1:48712 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:05 [logger.py:39] Received request chatcmpl-3af2646a109e44909091cca127098481: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Extract the following information from the presented texts: The name of the book, the author, the main character, the year of publication. Output in the format of "main character, book, author, year of publication", one book per line.\na) In the realm of wizarding literature, a true standout is the work of J.K. Rowling. One of her books that left an indelible mark is \'Harry Potter and the Philosopher\'s Stone\'. This iconic tale, published in 1997, tells the story of Harry, a young orphan who discovers his magical abilities on his 11th birthday. Soon, he finds himself at the Hogwarts School of Witchcraft and Wizardry, a place teeming with magic and adventure, located somewhere in Scotland.\nb) The magic of Middle-earth has entranced readers worldwide, thanks to the brilliance of J.R.R. Tolkien. In one of his seminal works, \'The Lord of the Rings: The Fellowship of the Ring\', published in 1954, we meet Frodo Baggins, a brave hobbit tasked with the perilous quest of destroying the One Ring. The epic journey takes him from the peaceful Shire to the tumultuous regions of Middle-earth.\nc) In a galaxy far, far away, the imagination of L.E. Starlighter gives us \'The Prism Galaxy Chronicles: The Awakening of the Starcaster\'. Published in 2028, the story is about Zylo, a humble spaceship mechanic, who unexpectedly discovers he\'s a Starcaster - a rare individual with the power to manipulate stardust. Set against the backdrop of an interstellar empire in turmoil, Zylo\'s destiny unfolds on numerous alien worlds, each with its unique cosmic charm.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Harry, Harry Potter and the Philosopher\'s Stone, J.K. Rowling, 1997\nFrodo, The Lord of the Rings: The Fellowship of the Ring, J.R.R. Tolkien, 1954\nZylo, The Prism Galaxy Chronicles: The Awakening of the Starcaster, L.E. Starlighter, 2028<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Reformulate your earlier reply, output it in JSON format and only include books published after 1980.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:05 [engine.py:310] Added request chatcmpl-3af2646a109e44909091cca127098481. INFO 06-19 06:40:07 [metrics.py:489] Avg prompt throughput: 185.0 tokens/s, Avg generation throughput: 119.4 tokens/s, Running: 56 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48632 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:07 [logger.py:39] Received request chatcmpl-40683ad1049649dfb8bd79fbc0567bde: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Given that f(x) = 4x^3 - 9x - 14, find the value of f(2).<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>To find the value of f(2), substitute x = 2 into the function:\n\nf(2) = 4(2)^3 - 9(2) - 14\nf(2) = 4(8) - 18 - 14\nf(2) = 32 - 18 - 14\nf(2) = 10\n\nTherefore, the value of f(2) is 10.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Find x such that f(x) = 0.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:07 [engine.py:310] Added request chatcmpl-40683ad1049649dfb8bd79fbc0567bde. INFO: 127.0.0.1:48732 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:07 [logger.py:39] Received request chatcmpl-8b03256cf90942178fda8d918ca3a544: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Identify the countries, their capitals, and the languages spoken in the following sentences. Output in JSON format.\na) Amidst the idyllic vistas, Copenhagen, Denmark\'s capital, captivates visitors with its thriving art scene and the enchanting Danish language spoken by its inhabitants.\nb) Within the enchanting realm of Eldoria, one discovers Avalore, a grandiose city that emanates an ethereal aura. Lumina, a melodious language, serves as the principal mode of communication within this mystical abode.\nc) Nestled amidst a harmonious blend of age-old customs and contemporary wonders, Buenos Aires, the capital of Argentina, stands as a bustling metropolis. It is a vibrant hub where the expressive Spanish language holds sway over the city\'s inhabitants.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>{\n "a": {\n "country": "Denmark",\n "capital": "Copenhagen",\n "language": "Danish"\n },\n "b": {\n "country": "Eldoria",\n "capital": "Avalore",\n "language": "Lumina"\n },\n "c": {\n "country": "Argentina",\n "capital": "Buenos Aires",\n "language": "Spanish"\n }\n}<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Come up with 3 similar examples in the YAML format.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:09 [engine.py:310] Added request chatcmpl-8b03256cf90942178fda8d918ca3a544. INFO: 127.0.0.1:48818 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:09 [logger.py:39] Received request chatcmpl-4505445366294c0bac151450a5fed5ab: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please explain the differences between exothermic and endothermic reactions, and include the criteria you used to distinguish between them. Additionally, please provide a real-world example to illustrate your explanation.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Exothermic and endothermic reactions are distinguished based on the energy change that occurs during the reaction. Exothermic reactions release heat into the surroundings, while endothermic reactions absorb heat from the surroundings. The criteria used to distinguish between them include the change in enthalpy (ΔH), which is a measure of the heat energy involved in the reaction. A negative ΔH indicates an exothermic reaction, while a positive ΔH indicates an endothermic reaction.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Can a process involve both reactions? List one.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:11 [engine.py:310] Added request chatcmpl-4505445366294c0bac151450a5fed5ab. INFO 06-19 06:40:14 [metrics.py:489] Avg prompt throughput: 192.3 tokens/s, Avg generation throughput: 17.8 tokens/s, Running: 57 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48782 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48848 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48388 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48688 - "POST /v1/chat/completions HTTP/1.1" 200 OK 30%|████████████████████████████████████████████████████▏ | 24/80 [00:56<02:33, 2.74s/it]INFO 06-19 06:40:14 [logger.py:39] Received request chatcmpl-5cfb6aeb31f94ded901b25b1761ab7ca: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How have the Alps and Rhine River influenced settlement and agriculture in Western Europe? List three impacts.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The Alps and Rhine River have influenced settlement and agriculture in Western Europe by:\n\n1. Creating natural barriers that have shaped the boundaries of countries and regions.\n2. Providing fertile valleys and plains for agriculture, such as the Rhine River Valley.\n3. Influencing the development of transportation networks, such as the Rhine River, which has facilitated trade and commerce.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How could you design a concrete but simple experiment to validate the first impact?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:14 [logger.py:39] Received request chatcmpl-f036cb51f07c49e39e19598504ee679e: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Given the following records of stock prices, extract the highest and lowest closing prices for each month in the year 2022. Return the results as a CSV string, with one line allocated for each month.\nDate,Open,High,Low,Close,Volume\n2022-01-01,150.02,155.28,148.50,153.80,15678900\n2022-01-02,154.32,157.25,153.48,156.25,19874500\n2022-02-01,160.50,163.28,159.50,161.80,14326700\n2022-02-02,161.80,164.25,161.30,163.90,17689200\n2022-03-01,165.40,168.35,163.10,166.80,16253400\n2022-03-02,167.00,169.85,165.50,168.20,19568100<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The highest and lowest closing prices for each month in the year 2022 are:\n\nDate,Highest Closing Price,Lowest Closing Price\n2022-01-01,153.80,148.50\n2022-02-01,161.80,159.50\n2022-03-01,166.80,163.10<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Do the same task again with the JSON format and round all numbers in your response to the nearest integers.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:14 [logger.py:39] Received request chatcmpl-46550d6f7c4d48e6b4962e8ea3e9f1cd: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a function to find the majority element in a given integer array using the Boyer-Moore Voting Algorithm.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Here is the code to solve this problem: \n```python\ndef find_majority_element(arr):\n count = 0\n candidate = None\n for num in arr:\n if count == 0:\n candidate = num\n count += 1 if num == candidate else -1\n count = 0\n for num in arr:\n if num == candidate:\n count += 1\n if count > len(arr) // 2:\n return candidate\n else:\n return None\n```<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How about finding the top-2 most occurring elements?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:14 [engine.py:310] Added request chatcmpl-5cfb6aeb31f94ded901b25b1761ab7ca. INFO 06-19 06:40:14 [engine.py:310] Added request chatcmpl-f036cb51f07c49e39e19598504ee679e. INFO 06-19 06:40:14 [engine.py:310] Added request chatcmpl-46550d6f7c4d48e6b4962e8ea3e9f1cd. INFO: 127.0.0.1:48268 - "POST /v1/chat/completions HTTP/1.1" 200 OK 31%|██████████████████████████████████████████████████████▍ | 25/80 [00:58<02:24, 2.63s/it]INFO: 127.0.0.1:48504 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48496 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48322 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48796 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48344 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:16 [logger.py:39] Received request chatcmpl-0bd3b62799224d31a516d2f25ee9ae81: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Photosynthesis is a vital process for life on Earth. Could you outline the two main stages of photosynthesis, including where they take place within the chloroplast, and the primary inputs and outputs for each stage?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Photosynthesis consists of two main stages: the light-dependent reactions and the light-independent reactions (Calvin cycle). The light-dependent reactions occur in the thylakoid membrane of the chloroplast, where light energy is converted into chemical energy in the form of ATP and NADPH. The light-independent reactions, also known as the Calvin cycle, occur in the stroma of the chloroplast, where ATP and NADPH are used to convert carbon dioxide into glucose.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How much energy can a tree produce through photosynthesis in its lifetime? Please provide an estimate using actual numerical values and thoroughly explain your thought process step-by-step.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:16 [logger.py:39] Received request chatcmpl-72686d433d134d7d86812f55951fdacd: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a descriptive paragraph about a bustling marketplace, incorporating sensory details such as smells, sounds, and visual elements to create an immersive experience for the reader.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The marketplace was alive with a cacophony of sounds - the clinking of copper coins, the bustling of the crowd, and the chatter of merchants calling out their prices. The smell of fresh bread wafted through the air, mingling with the sweet scent of honey and the pungent aroma of spices. The visual elements - the vibrant colors of the stalls, the busy crowd, and the bustling activity - all contributed to a rich sensory experience for the reader.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Rework your previous response. Begin each sentence with the subsequent letter of the alphabet, commencing from B.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:16 [engine.py:310] Added request chatcmpl-0bd3b62799224d31a516d2f25ee9ae81. INFO 06-19 06:40:16 [engine.py:310] Added request chatcmpl-72686d433d134d7d86812f55951fdacd. INFO: 127.0.0.1:48840 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48918 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48340 - "POST /v1/chat/completions HTTP/1.1" 200 OK 36%|███████████████████████████████████████████████████████████████ | 29/80 [01:01<01:17, 1.51s/it]INFO 06-19 06:40:18 [logger.py:39] Received request chatcmpl-8e0e76f55e8a450ca7be720d8e149868: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Describe five key principles in evaluating an argument in analytical writing.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Five key principles in evaluating an argument in analytical writing are:\n\n1. Identifying the claim: Recognizing the main argument or point being made.\n2. Understanding the evidence: Examining the facts, data, or examples provided to support the claim.\n3. Assessing the reasoning: Evaluating the logical connections between the evidence and the claim.\n4. Considering counterarguments: Acknowledging and addressing potential opposing viewpoints.\n5. Drawing conclusions: Summarizing the main points and reiterating the argument\'s strengths and weaknesses.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>With the listed principles, write a response in which you discuss what specific evidence is needed to evaluate the argument and explain how the evidence would weaken or strengthen the argument.\n\n===\n\nThe following is a memorandum from the advertising head of Zorblatt Animal Outlets, a chain operating thirty animal outlets globally.\n\n"Half a decade ago, our rival Aquatic Pavilion started publicizing in Rare Pets Digest periodical. Their overall sales have been consistently growing at a rate of 3-to-5 percent each year since then. In particular, the Aquatic Pavilion outlet in Harbor Town experienced even more significant growth, securing the title of the most frequented animal store in the United States the previous year. In contrast, our two Zorblatt outlets in Harbor Town have recorded a consistent drop in sales during the same duration. It is evident that we must promptly start featuring our own advertisements in Rare Pets Digest and other popular animal publications. If we take this step, we can confidently anticipate a reversal in this recent trend of decreasing sales and return to profitability."<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:18 [logger.py:39] Received request chatcmpl-f2d1bcd67ec940a59ac328a3f42878f2: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>You have been tasked with designing a solar-powered water heating system for a residential building. Describe the key components and considerations you would include in your design. Design a five-step workflow.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Designing a solar-powered water heating system for a residential building involves several key components and considerations. Here is a five-step workflow for designing such a system:\n\n1. Assess the building's energy needs and water usage patterns.\n2. Determine the appropriate solar collector type and size based on the building's energy needs and available space.\n3. Design the system's plumbing and electrical connections.\n4. Install the system and ensure it is properly integrated with the building's existing infrastructure.\n5. Test the system and make any necessary adjustments to ensure optimal performance.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>If the system is intended for a building with a capacity of 100 individuals, what would be the estimated budget for implementing this system?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:18 [engine.py:310] Added request chatcmpl-8e0e76f55e8a450ca7be720d8e149868. INFO 06-19 06:40:18 [engine.py:310] Added request chatcmpl-f2d1bcd67ec940a59ac328a3f42878f2. INFO 06-19 06:40:20 [metrics.py:489] Avg prompt throughput: 268.9 tokens/s, Avg generation throughput: 210.4 tokens/s, Running: 51 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48432 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48568 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:20 [logger.py:39] Received request chatcmpl-ca7a7953b11245e38db16212981b9875: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Act as a math teacher. I will provide some mathematical equations or concepts, and it will be your job to explain them in easy-to-understand terms. This could include providing step-by-step instructions for solving a problem, demonstrating various techniques with examples in everyday life or suggesting online resources for further study. My first request is "I need help understanding how probability works."<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Of course! Probability is a branch of mathematics that deals with the study of chance events. It is used in various areas of life such as insurance, finance, and games of chance. The probability of an event occurring can be calculated by dividing the number of favorable outcomes by the total number of possible outcomes. For example, if you flip a coin, there are two possible outcomes: heads or tails. If you want to calculate the probability of getting tails, you would divide the number of tails (1) by the total number of possible outcomes (2), which would give you a probability of 0.5.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What are the differences between Riemannian geometry and euclidean geometry?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. 38%|█████████████████████████████████████████████████████████████████▎ | 30/80 [01:03<01:21, 1.62s/it]INFO 06-19 06:40:20 [engine.py:310] Added request chatcmpl-ca7a7953b11245e38db16212981b9875. INFO: 127.0.0.1:48816 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:22 [logger.py:39] Received request chatcmpl-39c272e38b0b48c6ae2c5ad79af4e93c: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Describe the process and write out the balanced chemical equation for the reaction that occurs when solid calcium carbonate reacts with hydrochloric acid to form aqueous calcium chloride, carbon dioxide, and water. What type of reaction is this, and what observations might indicate that the reaction is taking place?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>When solid calcium carbonate reacts with hydrochloric acid, the reaction produces aqueous calcium chloride, carbon dioxide, and water. The balanced chemical equation for this reaction is:\n\nCaCO3(s) + 2HCl(aq) → CaCl2(aq) + H2O(l) + CO2(g)\n\nThis is a double displacement reaction, and the observations that might indicate that the reaction is taking place include the formation of bubbles (carbon dioxide), the release of heat, and the formation of a white precipitate (calcium chloride).<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How can we reverse this process?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:22 [engine.py:310] Added request chatcmpl-39c272e38b0b48c6ae2c5ad79af4e93c. INFO: 127.0.0.1:48538 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:25 [logger.py:39] Received request chatcmpl-0be8fdfaa85c4a9ca1b07e4de7b30256: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>The vertices of a triangle are at points (0, 0), (-1, 1), and (3, 3). What is the area of the triangle?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The area of the triangle can be calculated using the determinant formula:\n\nArea = |(x1(y2 - y3) + x2(y3 - y1) + x3(y1 - y2)) / 2|\n\nSubstituting the coordinates of the vertices:\n\nArea = |(0(1 - 3) + (-1)(3 - 0) + 3(0 - 1)) / 2|\nArea = |(-1 - 3) / 2|\nArea = |-4 / 2|\nArea = 2\n\nTherefore, the area of the triangle is 2 square units.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What's area of the circle circumscribing the triangle?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:25 [engine.py:310] Added request chatcmpl-0be8fdfaa85c4a9ca1b07e4de7b30256. INFO: 127.0.0.1:48528 - "POST /v1/chat/completions HTTP/1.1" 200 OK 39%|███████████████████████████████████████████████████████████████████▍ | 31/80 [01:07<01:45, 2.14s/it]INFO 06-19 06:40:27 [metrics.py:489] Avg prompt throughput: 186.6 tokens/s, Avg generation throughput: 115.0 tokens/s, Running: 49 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48334 - "POST /v1/chat/completions HTTP/1.1" 200 OK 40%|█████████████████████████████████████████████████████████████████████▌ | 32/80 [01:09<01:43, 2.15s/it]INFO: 127.0.0.1:48654 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48924 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:27 [logger.py:39] Received request chatcmpl-23a322f69f6944b1bf0af52bd23e7d56: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Which methods did Socrates employ to challenge the prevailing thoughts of his time?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Socrates employed various methods to challenge the prevailing thoughts of his time, including:\n\n1. The Socratic method: A method of inquiry used to stimulate critical thinking and to expose the contradictions in one's beliefs.\n2. The elenchos: A technique used to expose the contradictions in one's beliefs by asking a series of questions.\n3. The maieutic method: A method of inquiry used to help individuals discover their own knowledge and understanding.\n4. The use of irony and paradox: Socrates used irony and paradox to challenge the prevailing thoughts of his time and to expose the contradictions in one's beliefs.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Let's bring Socrates to modern world. Generate a conversation between Socrates and Bill Gates to debate on generative AI for education.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:27 [engine.py:310] Added request chatcmpl-23a322f69f6944b1bf0af52bd23e7d56. INFO: 127.0.0.1:48662 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48472 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48370 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:29 [logger.py:39] Received request chatcmpl-3e2cbb06000a4ba8adae0c03d4719874: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a function to find the highest common ancestor (not LCA) of two nodes in a binary tree.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Here is the code to solve this problem: \n```python\nclass TreeNode:\n def __init__(self, val=0, left=None, right=None):\n self.val = val\n self.left = left\n self.right = right\n\ndef find_highest_common_ancestor(root, node1, node2):\n if not root:\n return None\n\n if root.val == node1 or root.val == node2:\n return root\n\n left = find_highest_common_ancestor(root.left, node1, node2)\n right = find_highest_common_ancestor(root.right, node1, node2)\n\n if left and right:\n return root\n else:\n return left if left else right\n```<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What if it is not a binary tree?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. 42%|█████████████████████████████████████████████████████████████████████████▉ | 34/80 [01:12<01:21, 1.77s/it]INFO 06-19 06:40:29 [engine.py:310] Added request chatcmpl-3e2cbb06000a4ba8adae0c03d4719874. INFO: 127.0.0.1:48630 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:31 [logger.py:39] Received request chatcmpl-12b9c307edc746c0a9b86084efda208b: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Benjamin went to a bookstore and purchased a variety of books. He bought 5 copies of a sci-fi novel, each priced at $20, 3 copies of a history book priced at $30 each, and 2 copies of a philosophy book for $45 each.\nWhat was the total cost of his purchases?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>To calculate the total cost of Benjamin's purchases, we need to multiply the number of each type of book by its respective price and then sum up the costs.\n\nFor the sci-fi novels:\n5 copies * $20/copy = $100\n\nFor the history books:\n3 copies * $30/copy = $90\n\nFor the philosophy books:\n2 copies * $45/copy = $90\n\nNow, we add up the costs of all the books:\n$100 (sci-fi) + $90 (history) + $90 (philosophy) = $280\n\nThe total cost of Benjamin's purchases was $\\boxed{\\$280}$ .<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Suppose Benjamin decides to sell each of these books at a 25% markup from the price he purchased them. What would be his total revenue if he sold all the books he bought?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:31 [engine.py:310] Added request chatcmpl-12b9c307edc746c0a9b86084efda208b. INFO 06-19 06:40:33 [metrics.py:489] Avg prompt throughput: 117.1 tokens/s, Avg generation throughput: 161.3 tokens/s, Running: 45 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48388 - "POST /v1/chat/completions HTTP/1.1" 200 OK 45%|██████████████████████████████████████████████████████████████████████████████▎ | 36/80 [01:16<01:24, 1.92s/it]INFO: 127.0.0.1:48496 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48392 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48632 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48610 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48354 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48386 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:34 [logger.py:39] Received request chatcmpl-517b946ef1be41ebb2928c46236d54b2: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>When a number is divided by 10, the remainder is 4. What is the remainder when twice the number is divided by 4?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Let the number be $n$. Since $n$ leaves a remainder of 4 when divided by 10, we can express $n$ as $n = 10k + 4$ for some integer $k$.\n\nNow, we need to find the remainder when twice the number is divided by 4. We calculate $2n$ as follows:\n\\[ 2n = 2(10k + 4) = 20k + 8. \\]\n\nWhen $20k + 8$ is divided by 4, the term $20k$ is divisible by 4, leaving a remainder of 0. The term $8$ divided by 4 gives a remainder of 0. Therefore, the remainder when $2n$ is divided by 4 is $\\boxed{0}$.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What about when twice the number is divided by 5?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO: 127.0.0.1:48460 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:34 [engine.py:310] Added request chatcmpl-517b946ef1be41ebb2928c46236d54b2. INFO 06-19 06:40:34 [logger.py:39] Received request chatcmpl-a8ac58a530fa4994b250d52e4e8afad2: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Suppose you are a mathematician and poet. You always write your proofs as short poets with less than 10 lines but rhyme. Prove the square root of 2 is irrational number.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>A number, when squared, gives two,\nOf this, we're sure, it can not be false.\nIf it were, as rational,\nWe'd see it in its fractional form.\n\nLet's assume it's rational, true,\nSo let's write it as p/q, where p and q are two.\nNow, p squared equals two times q,\nMultiplying by two, we square the product.\n\nSo, p squared equals two times q,\nAnd q squared equals p squared, as we know,\nFrom this, we deduce that p squared equals two times p times q,\nWhich is not possible, as p squared is two times q, and q squared is p squared.\n\nThus, our assumption that it's rational,\nIs not true, and it's irrational.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Prove the Pythagorean theorem.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:36 [engine.py:310] Added request chatcmpl-a8ac58a530fa4994b250d52e4e8afad2. INFO: 127.0.0.1:48712 - "POST /v1/chat/completions HTTP/1.1" 200 OK 52%|███████████████████████████████████████████████████████████████████████████████████████████▎ | 42/80 [01:18<00:37, 1.02it/s]INFO: 127.0.0.1:48268 - "POST /v1/chat/completions HTTP/1.1" 200 OK 54%|█████████████████████████████████████████████████████████████████████████████████████████████▌ | 43/80 [01:20<00:41, 1.13s/it]INFO: 127.0.0.1:48564 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48784 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:38 [logger.py:39] Received request chatcmpl-7974e563f58c43e8a0f167f53c268bf5: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>In a survey conducted at a local high school, preferences for a new school color were measured: 58% of students liked the color blue, 45% preferred green, and 22% liked both colors. If we randomly pick a student from the school, what's the probability that they would like neither blue nor green?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>To find the probability that a randomly picked student would like neither blue nor green, we first need to find the probability of a student liking either blue or green. This can be calculated using the principle of inclusion-exclusion:\n\nP(Blue or Green) = P(Blue) + P(Green) - P(Blue and Green)\n= 0.58 + 0.45 - 0.22\n= 0.81\n\nNow, to find the probability of a student liking neither blue nor green, we subtract this value from 1:\n\nP(Neither Blue nor Green) = 1 - P(Blue or Green)\n= 1 - 0.81\n= 0.19\n\nSo, the probability that a randomly picked student would like neither blue nor green is 0.19 or 19%.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>If we select a student liked green, what's the probability that he or she would dislike both colors?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:38 [engine.py:310] Added request chatcmpl-7974e563f58c43e8a0f167f53c268bf5. INFO 06-19 06:40:38 [logger.py:39] Received request chatcmpl-da70ab81d5124d208f65fec857017468: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>In the field of quantum physics, what is superposition, and how does it relate to the phenomenon of quantum entanglement?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Superposition is a fundamental concept in quantum physics that describes the ability of a quantum system to exist in multiple states simultaneously. This is mathematically represented by a wave function, which is a linear combination of the system's possible states. When a measurement is made, the system collapses into one of the possible states, with the probability of each state determined by the square of the amplitude of its corresponding wave function component.\n\nQuantum entanglement is a phenomenon that occurs when two or more quantum systems become correlated in such a way that the state of one system cannot be described independently of the state of the other. This correlation is not limited by distance and can be observed even when the entangled systems are separated by large distances. The phenomenon of quantum entanglement is closely related to the concept of superposition, as the entangled systems can be thought of as being in a superposition of correlated states.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What assumptions have you made in your response? Are they valid?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:39 [metrics.py:489] Avg prompt throughput: 163.2 tokens/s, Avg generation throughput: 146.3 tokens/s, Running: 36 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%. INFO 06-19 06:40:40 [engine.py:310] Added request chatcmpl-da70ab81d5124d208f65fec857017468. INFO: 127.0.0.1:48528 - "POST /v1/chat/completions HTTP/1.1" 200 OK 55%|███████████████████████████████████████████████████████████████████████████████████████████████▋ | 44/80 [01:25<00:58, 1.63s/it]INFO: 127.0.0.1:48558 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48782 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48938 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48688 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48298 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:42 [logger.py:39] Received request chatcmpl-add5ea49147b4843aa3dc3d16639dbd7: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Suggest five award-winning documentary films with brief background descriptions for aspiring filmmakers to study.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>1. "An Inconvenient Truth" (2006) - A documentary about former Vice President Al Gore\'s efforts to educate the public about global warming.\n2. "Blackfish" (2013) - An investigative documentary about the treatment of orcas at SeaWorld and other marine parks.\n3. "The Cove" (2009) - A film that exposes the annual dolphin hunt in Taiji, Japan.\n4. "The Square" (2013) - A documentary about the Egyptian Revolution of 2011 and the role of Tahrir Square in the protests.\n5. "The Act of Killing" (2012) - A film about the Indonesian mass killings of 1965-66 and the perpetrators\' reflections on their actions.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>With the spirit in the first film, craft a succinct and persuasive pitch for a film about overcoming adversity.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:42 [engine.py:310] Added request chatcmpl-add5ea49147b4843aa3dc3d16639dbd7. 59%|██████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 47/80 [01:25<00:33, 1.00s/it]INFO 06-19 06:40:42 [logger.py:39] Received request chatcmpl-0ac8bf8a0d494b56a00339c4962318ce: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Imagine you are writing a blog post comparing two popular smartphone models. Develop an outline for the blog post, including key points and subheadings to effectively compare and contrast the features, performance, and user experience of the two models. Please answer in fewer than 200 words.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Outline for comparison of two popular smartphone models:\n\nI. Introduction\n - Brief introduction of the two smartphone models\n - Purpose of the comparison\n\nII. Design and Build Quality\n - Materials used\n - Dimensions and weight\n - Ergonomics and build quality\n\nIII. Display\n - Screen size and resolution\n - Display technology\n - Brightness and viewing angles\n\nIV. Performance\n - Processor and memory\n - Battery life and charging\n - Software and operating system\n\nV. Camera\n - Camera resolution and sensor\n - Optical image stabilization\n - Low light performance\n\nVI. Connectivity\n - Wi-Fi and Bluetooth\n - NFC and mobile payments\n - 5G and network speeds\n\nVII. User Experience\n - User interface and navigation\n - Additional features and software\n\nVIII. Conclusion\n - Final thoughts and recommendations\n - Comparison of the two models based on the comparison points.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Take your previous response and rephrase it as a limerick.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:45 [metrics.py:489] Avg prompt throughput: 120.2 tokens/s, Avg generation throughput: 111.9 tokens/s, Running: 32 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%. INFO 06-19 06:40:45 [engine.py:310] Added request chatcmpl-0ac8bf8a0d494b56a00339c4962318ce. INFO: 127.0.0.1:48648 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:47 [logger.py:39] Received request chatcmpl-483960a63e294aa8adbb2d189c48b81a: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a C++ program to find the nth Fibonacci number using recursion.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Here is a C++ program to find the nth Fibonacci number using recursion:\n\n```cpp\n#include \n\nint fibonacci(int n) {\n if (n <= 1) {\n return n;\n } else {\n return fibonacci(n - 1) + fibonacci(n - 2);\n }\n}\n\nint main() {\n int n;\n std::cout << "Enter the value of n: ";\n std::cin >> n;\n std::cout << "The " << n << "th Fibonacci number is: " << fibonacci(n) << std::endl;\n return 0;\n}\n```\n\nThis program uses a recursive function `fibonacci` to calculate the nth Fibonacci number. The base case is when `n` is 0 or 1, in which case the function returns `n`. Otherwise, it recursively calls itself with `n-1` and `n-2` and returns the sum of the two results.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Now we define a sequence of numbers in which each number is the sum of the three preceding ones. The first three numbers are 0, -1, -1. Write a program to find the nth number.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:47 [engine.py:310] Added request chatcmpl-483960a63e294aa8adbb2d189c48b81a. INFO: 127.0.0.1:48932 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:49 [logger.py:39] Received request chatcmpl-8ad58bb49b514e44aac66b01d4206cd3: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What are some business etiquette norms when doing business in Japan?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Some business etiquette norms when doing business in Japan include:\n\n1. Bowing: Bowing is a common greeting in Japan, and the depth and duration of the bow indicate the level of respect and formality.\n2. Gift-giving: Gift-giving is an important aspect of Japanese business culture, and gifts should be given with both hands and received with both hands.\n3. Business cards: Business cards are exchanged during introductions and should be treated with respect.\n4. Table manners: Table manners are important in Japan, and it is customary to wait for the host to begin eating before starting.\n5. Punctuality: Punctuality is highly valued in Japan, and it is important to arrive on time for meetings and appointments.\n6. Respect for hierarchy: Respect for hierarchy is important in Japanese business culture, and it is customary to address superiors with respectful titles and to show deference to their opinions.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Create a video script for training new employees of a car wash business in Japan. Highlight the above etiquette norms.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:49 [engine.py:310] Added request chatcmpl-8ad58bb49b514e44aac66b01d4206cd3. INFO 06-19 06:40:51 [metrics.py:489] Avg prompt throughput: 153.3 tokens/s, Avg generation throughput: 99.9 tokens/s, Running: 33 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48676 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48674 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:51 [logger.py:39] Received request chatcmpl-5789950e3cf345ca9d40f0b966ca8e68: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>You are given two sorted lists of size m and n. Implement a function to find the kth smallest element in the union of the two lists with linear complexity.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Here is a Python implementation of the function to find the kth smallest element in the union of two sorted lists with linear complexity:\n\n```python\ndef find_kth_smallest(arr1, arr2, k):\n i = j = 0\n while k > 0:\n if i < len(arr1) and j < len(arr2):\n if arr1[i] < arr2[j]:\n k -= 1\n i += 1\n else:\n k -= 1\n j += 1\n elif i < len(arr1):\n i += 1\n elif j < len(arr2):\n j += 1\n return arr1[i-1] if i > 0 else arr2[j-1]\n```\n\nThis function uses two pointers, `i` and `j`, to traverse the two sorted lists, `arr1` and `arr2`, respectively. It compares the elements at the current positions of the pointers and moves the pointer of the list with the smaller element. This process continues until the kth smallest element is found.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Does there exist an algorithm with better time complexity? If so, implement it.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:51 [logger.py:39] Received request chatcmpl-3fba501bb17f440884aad66cf3a8b3fe: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>A binary tree is full if all of its vertices have either zero or two children. Let B_n denote the number of full binary trees with n vertices. Implement a function to find B_n.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Here is a Python function to find B_n:\n\n```python\ndef B_n(n):\n if n == 0:\n return 1\n if n == 1:\n return 1\n if n == 2:\n return 2\n B = [0] * (n + 1)\n B[0] = 1\n B[1] = 1\n B[2] = 2\n for i in range(3, n + 1):\n B[i] = B[i - 1] + B[i - 2]\n return B[n]\n```\n\nThis function uses dynamic programming to calculate the number of full binary trees with n vertices. It initializes a list B with 0s and sets the base cases B[0] = 1, B[1] = 1, and B[2] = 2. Then, it iterates from 3 to n and calculates B[i] as the sum of B[i - 1] and B[i - 2]. Finally, it returns B[n].<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>What if the problem changed from a binary tree to a ternary tree?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:51 [engine.py:310] Added request chatcmpl-5789950e3cf345ca9d40f0b966ca8e68. INFO 06-19 06:40:51 [engine.py:310] Added request chatcmpl-3fba501bb17f440884aad66cf3a8b3fe. INFO: 127.0.0.1:48646 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:53 [logger.py:39] Received request chatcmpl-58e3d4fa939847609fb3b7c1c67debf9: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Develop a Python program that reads all the text files under a directory and returns top-5 words with the most number of occurrences.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Certainly! Here's a Python program that reads all the text files under a directory and returns the top-5 words with the most number of occurrences:\n\n```python\nimport os\nfrom collections import Counter\n\ndef get_top_5_words(directory):\n word_counts = Counter()\n for filename in os.listdir(directory):\n if filename.endswith('.txt'):\n with open(os.path.join(directory, filename), 'r') as file:\n text = file.read().lower()\n words = text.split()\n word_counts.update(words)\n return word_counts.most_common(5)\n\n# Example usage\ndirectory = '/path/to/directory'\ntop_5_words = get_top_5_words(directory)\nprint(top_5_words)\n```\n\nThis program uses the `os` module to list all the files in the specified directory and the `collections.Counter` class to count the occurrences of each word. The `get_top_5_words` function returns the top-5 words with the most number of occurrences.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Can you parallelize it?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:53 [engine.py:310] Added request chatcmpl-58e3d4fa939847609fb3b7c1c67debf9. INFO: 127.0.0.1:48838 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:56 [logger.py:39] Received request chatcmpl-2441577083904922915c6f1cf24f97e9: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please describe the concept of machine learning. Could you elaborate on the differences between supervised, unsupervised, and reinforcement learning? Provide real-world examples of each.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Machine learning is a subset of artificial intelligence that involves training algorithms to learn from data and make predictions or decisions without being explicitly programmed. The three main types of machine learning are supervised, unsupervised, and reinforcement learning.\n\nSupervised learning involves training a model on labeled data, where the input and output are both known. The model learns to map inputs to outputs based on the training data. For example, a model trained on labeled images of cats and dogs can learn to classify new images as either a cat or a dog.\n\nUnsupervised learning involves training a model on unlabeled data, where only the input is known. The model learns to identify patterns or structure in the data without any prior knowledge of the output. For example, a model trained on a dataset of customer transactions can learn to identify clusters of customers with similar purchasing behavior.\n\nReinforcement learning involves training a model to make decisions based on rewards or penalties. The model learns to maximize its rewards by trying different actions and learning from the outcomes. For example, a model trained to play a game can learn to make moves that maximize its score by trying different strategies and learning from the outcomes.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>In your last example of reinforcement learning, can we use supervised learning to solve it?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:56 [engine.py:310] Added request chatcmpl-2441577083904922915c6f1cf24f97e9. INFO 06-19 06:40:57 [metrics.py:489] Avg prompt throughput: 237.7 tokens/s, Avg generation throughput: 163.8 tokens/s, Running: 33 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.2%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48896 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:40:58 [logger.py:39] Received request chatcmpl-1b32a19c3ea94597b30a81dfce44111b: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Share ideas for adapting art masterpieces into interactive experiences for children. List 5 specific artworks and associated ideas.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>1. "The Starry Night" by Vincent van Gogh: Create an immersive experience where children can paint their own starry night sky using glow-in-the-dark paints and LED lights.\n2. "The Persistence of Memory" by Salvador Dalí: Design a surreal playground with melting clocks and interactive elements that encourage children to explore and engage with the artwork.\n3. "The Raft of the Medusa" by Théodore Géricault: Develop an interactive museum exhibit where children can participate in a simulated rescue mission, learning about the artwork\'s historical context and the artist\'s intentions.\n4. "The Last Supper" by Leonardo da Vinci: Create an augmented reality experience where children can step into the painting and interact with the figures, learning about the artwork\'s symbolism and cultural significance.\n5. "The Weeping Woman" by Pablo Picasso: Design a sensory-rich environment where children can explore the emotions and themes of the painting through tactile and auditory elements, such as soft fabrics and soothing sounds.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a concrete plan for your second example. Include budget estimates.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:40:58 [engine.py:310] Added request chatcmpl-1b32a19c3ea94597b30a81dfce44111b. INFO: 127.0.0.1:48298 - "POST /v1/chat/completions HTTP/1.1" 200 OK 60%|████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 48/80 [01:42<01:51, 3.47s/it]INFO: 127.0.0.1:48340 - "POST /v1/chat/completions HTTP/1.1" 200 OK 61%|██████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 49/80 [01:43<01:33, 3.00s/it]INFO: 127.0.0.1:48744 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48902 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48386 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48528 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:41:00 [logger.py:39] Received request chatcmpl-f47f0351257d441c95dad3caa90e38a5: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Explain what's base rate fallacy and list five specific examples of how politicians use it for campaigns.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The base rate fallacy is a cognitive bias that occurs when people ignore the base rate (the probability of an event occurring in the general population) and focus on specific information, leading to incorrect conclusions. Politicians often use this fallacy to manipulate public opinion by emphasizing specific cases rather than the overall statistics. Five examples of this are:\n\n1. Politicians may claim that a specific policy has been successful in reducing crime rates, ignoring the fact that crime rates have been declining nationwide due to various factors.\n2. They may highlight the success of a single business in a particular region, ignoring the fact that the overall economy is not doing well.\n3. Politicians may use anecdotal evidence of a single person's success with a policy, ignoring the fact that the policy may not be effective for the general population.\n4. They may emphasize the success of a specific program in a particular community, ignoring the fact that the program may not be effective for other communities.\n5. Politicians may use the base rate fallacy to create fear and anxiety among the public by emphasizing specific threats, such as terrorism or immigration, rather than the overall statistics.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Provide a detailed plan for an election campaign using the first example.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:41:00 [logger.py:39] Received request chatcmpl-1366b089856f4c859d3f4f048f12bda9: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please read the paragraph below and count how many times the words "Amazon", "river", and "you" appear. Please present the results in the format of "word, number of appearances" with each word on a separate line. Sort the lines in order of the number of appearances.\nThe Amazon, a mesmerizing expanse of nature\'s wonders, is home to the legendary Amazon River. Flowing through awe-inspiring landscapes like the Amazon rainforest, the river weaves its way through Brazil, Colombia, and Peru, giving life to countless creatures. From the mighty jaguars prowling the Amazon jungle to the vibrant macaws soaring above the canopy, this remarkable region teems with biodiversity. Deep within the river\'s currents, magnificent pink river dolphins gracefully glide alongside piranhas and electric eels. Along the riverbanks, you\'ll find bustling cities like Manaus, where the urban meets the wild, and Iquitos, a gateway to the heart of the Amazon rainforest. As you venture further, the Amazon River reveals hidden gems like the captivating Anavilhanas Archipelago, a mosaic of islands brimming with rare species. Embark on an adventure, explore the enchanting Amazon River, and immerse yourself in a world teeming with life and untamed beauty.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>The Amazon, a mesmerizing expanse of nature\'s wonders, is home to the legendary Amazon River. Flowing through awe-inspiring landscapes like the Amazon rainforest, the river weaves its way through Brazil, Colombia, and Peru, giving life to countless creatures. From the mighty jaguars prowling the Amazon jungle to the vibrant macaws soaring above the canopy, this remarkable region teems with biodiversity. Deep within the river\'s currents, magnificent pink river dolphins gracefully glide alongside piranhas and electric eels. Along the riverbanks, you\'ll find bustling cities like Manaus, where the urban meets the wild, and Iquitos, a gateway to the heart of the Amazon rainforest. As you venture further, the Amazon River reveals hidden gems like the captivating Anavilhanas Archipelago, a mosaic of islands brimming with rare species. Embark on an adventure, explore the enchanting Amazon River, and immerse yourself in a world teeming with life and untamed beauty.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Please repeat the same task using the words \'the\', \'and\', and \'to\'<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:41:00 [engine.py:310] Added request chatcmpl-f47f0351257d441c95dad3caa90e38a5. INFO 06-19 06:41:00 [engine.py:310] Added request chatcmpl-1366b089856f4c859d3f4f048f12bda9. INFO 06-19 06:41:02 [metrics.py:489] Avg prompt throughput: 143.3 tokens/s, Avg generation throughput: 90.5 tokens/s, Running: 29 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48890 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:41:02 [logger.py:39] Received request chatcmpl-c073009eb2d845859734d5d42cf1a2be: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Create a lesson plan that integrates drama, mime or theater techniques into a history class. Duration: 3 class periods (each lasts for 45 minutes) for 3 days\nTopic: Opium Wars between China and Britain\nGrade level: 9-10<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Lesson Plan:\n\nDay 1:\n- Introduction to the topic of the Opium Wars (45 minutes)\n- Students will be divided into groups of 4-5 and will be given a scenario to act out. They will have to use drama, mime, or theater techniques to depict the scenario.\n- Students will present their acted-out scenarios to the class.\n- The class will discuss the scenarios and the historical context of the Opium Wars.\n\nDay 2:\n- Students will be given a script to act out a scene from the Opium Wars. They will have to use drama, mime, or theater techniques to depict the scene.\n- Students will present their acted-out scenes to the class.\n- The class will discuss the scenes and the historical context of the Opium Wars.\n\nDay 3:\n- Students will be given a research project to complete on the Opium Wars. They will have to use drama, mime, or theater techniques to present their findings to the class.\n- Students will present their findings to the class.\n- The class will discuss the findings and the historical context of the Opium Wars.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Provide more details for Day 1 and include three homework questions.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.1, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:41:02 [engine.py:310] Added request chatcmpl-c073009eb2d845859734d5d42cf1a2be. INFO: 127.0.0.1:48432 - "POST /v1/chat/completions HTTP/1.1" 200 OK 65%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 52/80 [01:47<01:03, 2.28s/it]INFO: 127.0.0.1:48392 - "POST /v1/chat/completions HTTP/1.1" 200 OK 66%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 53/80 [01:47<00:52, 1.95s/it]INFO: 127.0.0.1:48600 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:41:05 [logger.py:39] Received request chatcmpl-90a6d2b3e4b84f4a9f009803a1a05b13: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Some people got on a bus at the terminal. At the first bus stop, half of the people got down and 4 more people got in. Then at the second bus stop, 6 people got down and 8 more got in. If there were a total of 25 people heading to the third stop, how many people got on the bus at the terminal?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Let's denote the number of people who got on the bus at the terminal as x.\n\nAt the first bus stop, half of the people got down, so there were x/2 people left on the bus. Then 4 more people got in, so the number of people on the bus became x/2 + 4.\n\nAt the second bus stop, 6 people got down, so there were x/2 + 4 - 6 people left on the bus. Then 8 more people got in, so the number of people on the bus became (x/2 + 4 - 6) + 8.\n\nWe know that there were a total of 25 people heading to the third stop, so we can set up the equation:\n\n(x/2 + 4 - 6) + 8 = 25\n\nSimplifying the equation:\n\n(x/2 - 2) + 8 = 25\n\n(x/2 - 2) = 25 - 8\n\n(x/2 - 2) = 17\n\n(x/2) = 17 + 2\n\n(x/2) = 19\n\nx = 19 * 2\n\nx = 38\n\nSo, $\\boxed{38}$ people got on the bus at the terminal.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>If the ticket is $2 per person, how much is the total money earned by the bus?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:41:05 [engine.py:310] Added request chatcmpl-90a6d2b3e4b84f4a9f009803a1a05b13. INFO: 127.0.0.1:48268 - "POST /v1/chat/completions HTTP/1.1" 200 OK 68%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 54/80 [01:49<00:53, 2.04s/it]INFO: 127.0.0.1:48446 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48318 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:41:07 [metrics.py:489] Avg prompt throughput: 383.2 tokens/s, Avg generation throughput: 283.6 tokens/s, Running: 23 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48652 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:41:07 [logger.py:39] Received request chatcmpl-39f7941ffef54adca1aec71300925ce2: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a simple website in HTML. When a user clicks the button, it shows a random joke from a list of 4 jokes.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Here\'s a simple website in HTML that shows a random joke from a list of 4 jokes when the button is clicked:\n\n```html\n\n\n\n Random Joke Generator\n\n\n

Random Joke Generator

\n \n

\n\n \n\n\n```\n\nThis code creates a simple website with a button that, when clicked, shows a random joke from the `jokes` array. The `showRandomJoke` function is called when the button is clicked, and it selects a random index from the `jokes` array using `Math.random()` and `Math.floor()`. The selected joke is then displayed in the paragraph with the id `joke`.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>How to use CSS to change the color of jokes to red?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:41:07 [engine.py:310] Added request chatcmpl-39f7941ffef54adca1aec71300925ce2. INFO: 127.0.0.1:48468 - "POST /v1/chat/completions HTTP/1.1" 200 OK 71%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉ | 57/80 [01:52<00:33, 1.47s/it]INFO: 127.0.0.1:48838 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48690 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:41:09 [logger.py:39] Received request chatcmpl-43fb388f5a134a4688306a839b3410f6: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Implement a program to find the common elements in two arrays without using any extra data structures.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Certainly! Here's an example program that finds the common elements in two arrays without using any extra data structures:\n\n```python\ndef find_common_elements(arr1, arr2):\n common_elements = []\n for i in arr1:\n if i in arr2:\n common_elements.append(i)\n return common_elements\n\narr1 = [1, 2, 3, 4, 5]\narr2 = [4, 5, 6, 7, 8]\n\ncommon_elements = find_common_elements(arr1, arr2)\nprint(common_elements)\n```\n\nOutput:\n```\n[4, 5]\n```\n\nIn this program, we define a function `find_common_elements` that takes two arrays as input. We initialize an empty list `common_elements` to store the common elements. Then, we iterate over each element `i` in `arr1`. Inside the loop, we check if `i` is present in `arr2` using the `in` operator. If it is, we append `i` to the `common_elements` list. Finally, we return the `common_elements` list.\n\nIn the main program, we create two arrays `arr1` and `arr2` with some sample elements. We then call the `find_common_elements` function with these arrays as arguments and store the result in the `common_elements` variable. Finally, we print the `common_elements` list, which contains the common elements between the two arrays.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Now the constraint of not using extra data structure is removed, implement one with the best time complexity.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:41:09 [engine.py:310] Added request chatcmpl-43fb388f5a134a4688306a839b3410f6. INFO: 127.0.0.1:48392 - "POST /v1/chat/completions HTTP/1.1" 200 OK 74%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▎ | 59/80 [01:53<00:25, 1.23s/it]INFO: 127.0.0.1:48354 - "POST /v1/chat/completions HTTP/1.1" 200 OK 75%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 60/80 [01:54<00:23, 1.18s/it]INFO: 127.0.0.1:48538 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48322 - "POST /v1/chat/completions HTTP/1.1" 200 OK 78%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 62/80 [01:54<00:14, 1.26it/s]INFO: 127.0.0.1:48268 - "POST /v1/chat/completions HTTP/1.1" 200 OK 79%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 63/80 [01:54<00:11, 1.49it/s]INFO: 127.0.0.1:48558 - "POST /v1/chat/completions HTTP/1.1" 200 OK 80%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 64/80 [01:55<00:09, 1.77it/s]INFO 06-19 06:41:12 [metrics.py:489] Avg prompt throughput: 175.3 tokens/s, Avg generation throughput: 341.7 tokens/s, Running: 16 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48302 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48344 - "POST /v1/chat/completions HTTP/1.1" 200 OK 81%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▍ | 65/80 [01:55<00:07, 2.08it/s]INFO 06-19 06:41:12 [logger.py:39] Received request chatcmpl-5501afd21ecc44c48070479201d7b58b: prompt: "<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Write a persuasive email to convince your introverted friend, who dislikes public speaking, to volunteer as a guest speaker at a local event. Use compelling arguments and address potential objections. Please be concise.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Subject: Volunteer as a Guest Speaker at Local Event - It's a Great Opportunity!\n\nDear [Friend's Name],\n\nI hope this email finds you well. I wanted to reach out and share an exciting opportunity with you that I believe could be a fantastic fit for your skills and passions. I recently came across a local event seeking guest speakers, and I immediately thought of you.\n\nI know you've expressed discomfort with public speaking in the past, but this event could be a perfect chance to step out of your comfort zone and share your expertise with others. You have a unique perspective and valuable knowledge to offer, and I firmly believe that your presence would be highly appreciated and beneficial to the attendees.\n\nHere are some compelling reasons why I think you should consider this opportunity:\n\n1. Help others: By sharing your insights, you can inspire and educate the audience, making a positive impact on their lives.\n2. Networking: This event is a great platform to connect with like-minded individuals and expand your professional network.\n3. Personal growth: Overcoming your fear of public speaking will not only boost your confidence but also enhance your overall personal development.\n4. Community involvement: Volunteering as a guest speaker allows you to contribute to the local community, which can be a rewarding experience in itself.\n\nI understand that public speaking can be intimidating, but I believe in your abilities and want to support you in this endeavor. If you're still unsure, let's discuss potential objections and explore ways to make this experience more comfortable for you.\n\nIf you're interested, please let me know, and I'll be happy to help you with the application process. Your involvement in this event will undoubtedly be a valuable experience for you and a significant contribution to the local community.\n\nThank you for considering this opportunity, and I look forward to hearing your thoughts.\n\nBest regards,\n\n[Your Name]<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Can you rephrase your previous answer and incorporate a metaphor or simile in each sentence?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>", params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:41:12 [engine.py:310] Added request chatcmpl-5501afd21ecc44c48070479201d7b58b. INFO: 127.0.0.1:48340 - "POST /v1/chat/completions HTTP/1.1" 200 OK 82%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 66/80 [01:57<00:12, 1.09it/s]INFO: 127.0.0.1:48646 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48408 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48284 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:41:14 [logger.py:39] Received request chatcmpl-f2f96ca579dd4a2c9ce2a0257fca092d: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Compose an engaging travel blog post about a recent trip to Hawaii, highlighting cultural experiences and must-see attractions.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Sure, here\'s an engaging travel blog post about a trip to Hawaii:\n\n"It\'s Hawaii Time! Adventure Awaits in the Pacific Paradise"\n\n"Hawaii has it all - stunning beaches, rich culture, and a heart-warming hospitality. We recently embarked on a journey to explore the Aloha State, and here are some of the most memorable moments that left us awestruck and inspired."\n\n"Cultural Experiences"\n\n"Our day began with a traditional Hawaiian luau, where we savored local delicacies like kalua pig, poi, and haupia, accompanied by live music and hula dancing. We also visited the Polynesian Cultural Center, an immersive experience that taught us about the diverse Pacific Islander cultures."\n\n"Must-See Attractions"\n\n"From the breathtaking beauty of Waimea Canyon, often referred to as the Grand Canyon of the Pacific, to the majestic Volcanoes National Park, where we witnessed the awe-inspiring power of Kilauea, our trip was filled with unforgettable experiences."\n\n"Adventure Awaits"\n\n"We embarked on a snorkeling adventure in Hanauma Bay, where we swam with colorful tropical fish and sea turtles. We also hiked the scenic Pipiwai Trail, a journey that led us to the breathtaking Waimoku Falls."\n\n"A Paradise of Endless Opportunities"\n\n"Hawaii is a place where adventure and relaxation go hand in hand. Whether you\'re a thrill-seeker or a nature enthusiast, there\'s always something new to discover in this Pacific paradise. So pack your bags and get ready for an unforgettable journey to the islands of Hawaii!"<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Rewrite your previous response. Start every sentence with the letter A.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.7, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:41:14 [engine.py:310] Added request chatcmpl-f2f96ca579dd4a2c9ce2a0257fca092d. INFO: 127.0.0.1:48334 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48386 - "POST /v1/chat/completions HTTP/1.1" 200 OK 86%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████ | 69/80 [01:59<00:09, 1.20it/s]INFO: 127.0.0.1:48370 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48674 - "POST /v1/chat/completions HTTP/1.1" 200 OK 90%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▌ | 72/80 [01:59<00:04, 1.94it/s]INFO 06-19 06:41:17 [metrics.py:489] Avg prompt throughput: 223.2 tokens/s, Avg generation throughput: 202.3 tokens/s, Running: 8 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.1%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48676 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48298 - "POST /v1/chat/completions HTTP/1.1" 200 OK 91%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 73/80 [02:00<00:03, 2.05it/s]INFO: 127.0.0.1:48660 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO 06-19 06:41:17 [logger.py:39] Received request chatcmpl-4865b47663f64ab194f94eb485ebff13: prompt: '<|start_of_role|>system<|end_of_role|>You are an AI language model developed by IBM Research. You are a cautious assistant. You carefully follow instructions. You are helpful and harmless and you follow ethical guidelines and promote positive behavior.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Implement a function to find the median of two sorted arrays of different sizes with O(1) space complexity and O(n) time complexity.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>Here\'s an implementation of the function to find the median of two sorted arrays of different sizes with O(1) space complexity and O(n) time complexity:\n\n```python\ndef find_median_sorted_arrays(nums1, nums2):\n if len(nums1) > len(nums2):\n return find_median_sorted_arrays(nums2, nums1)\n\n x, y = len(nums1), len(nums2)\n low, high = 0, x\n\n while low <= high:\n partition_x = (low + high) // 2\n partition_y = (x + y + 1) // 2 - partition_x\n\n max_left_x = float(\'-inf\') if partition_x == 0 else nums1[partition_x - 1]\n min_right_x = float(\'inf\') if partition_x == x else nums1[partition_x]\n\n max_left_y = float(\'-inf\') if partition_y == 0 else nums2[partition_y - 1]\n min_right_y = float(\'inf\') if partition_y == y else nums2[partition_y]\n\n if max_left_x <= min_right_y and max_left_y <= min_right_x:\n if (x + y) % 2 == 0:\n return (max(max_left_x, max_left_y) + min(min_right_x, min_right_y)) / 2\n else:\n return max(max_left_x, max_left_y)\n elif max_left_x > min_right_y:\n high = partition_x - 1\n else:\n low = partition_x + 1\n\n raise ValueError("Input arrays are not sorted")\n```\n\nThis function uses a binary search approach to find the median of the two sorted arrays. It first checks if the lengths of the two arrays are equal, and if not, it swaps them to ensure that the first array is the smaller one. Then, it uses a binary search to find the partition points in both arrays. Finally, it checks if the maximum element in the left half of the first array is less than or equal to the minimum element in the right half of the second array, and vice versa. If both conditions are true, it calculates the median based on the partition points and the lengths of the arrays. If not, it adjusts the binary search range and repeats the process until the median is found.<|end_of_text|>\n<|start_of_role|>user<|end_of_role|>Does there exist an implementation with better time complexity?<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>', params: SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.0, temperature=0.0, top_p=1.0, top_k=-1, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=1024, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=True, spaces_between_special_tokens=True, truncate_prompt_tokens=None, guided_decoding=None, extra_args=None), prompt_token_ids: None, lora_request: None, prompt_adapter_request: None. INFO 06-19 06:41:17 [engine.py:310] Added request chatcmpl-4865b47663f64ab194f94eb485ebff13. INFO: 127.0.0.1:48298 - "POST /v1/chat/completions HTTP/1.1" 200 OK 94%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▏ | 75/80 [02:03<00:04, 1.10it/s]INFO: 127.0.0.1:48896 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48318 - "POST /v1/chat/completions HTTP/1.1" 200 OK INFO: 127.0.0.1:48648 - "POST /v1/chat/completions HTTP/1.1" 200 OK 98%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋ | 78/80 [02:04<00:01, 1.78it/s]INFO: 127.0.0.1:48284 - "POST /v1/chat/completions HTTP/1.1" 200 OK 99%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊ | 79/80 [02:04<00:00, 1.70it/s]INFO 06-19 06:41:22 [metrics.py:489] Avg prompt throughput: 128.9 tokens/s, Avg generation throughput: 340.7 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%. INFO: 127.0.0.1:48268 - "POST /v1/chat/completions HTTP/1.1" 200 OK 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [02:05<00:00, 1.66it/s] 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 80/80 [02:05<00:00, 1.57s/it] INFO 06-19 06:41:22 [launcher.py:74] Shutting down FastAPI HTTP server. INFO 06-19 06:41:22 [multiproc_worker_utils.py:137] Terminating local vLLM worker processes (VllmWorkerProcess pid=91) INFO 06-19 06:41:22 [multiproc_worker_utils.py:259] Worker exiting (VllmWorkerProcess pid=96) INFO 06-19 06:41:22 [multiproc_worker_utils.py:259] Worker exiting (VllmWorkerProcess pid=92) INFO 06-19 06:41:22 [multiproc_worker_utils.py:259] Worker exiting (VllmWorkerProcess pid=97) INFO 06-19 06:41:22 [multiproc_worker_utils.py:259] Worker exiting (VllmWorkerProcess pid=94) INFO 06-19 06:41:22 [multiproc_worker_utils.py:259] Worker exiting (VllmWorkerProcess pid=93) INFO 06-19 06:41:22 [multiproc_worker_utils.py:259] Worker exiting (VllmWorkerProcess pid=95) INFO 06-19 06:41:22 [multiproc_worker_utils.py:259] Worker exiting [rank0]:[W619 06:41:24.304326025 ProcessGroupNCCL.cpp:1496] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator()) INFO: Shutting down INFO: Waiting for application shutdown. INFO: Application shutdown complete. /usr/lib64/python3.11/multiprocessing/resource_tracker.py:254: UserWarning: resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown warnings.warn('resource_tracker: There appear to be %d ' INFO 2025-06-19 06:41:27,117 instructlab.model.backends.vllm:512: Waiting for GPU VRAM reclamation... INFO 2025-06-19 06:42:05,990 instructlab.model.evaluate:200: Evaluating answers... WARNING 2025-06-19 06:42:06,042 instructlab.model.evaluate:773: Using gpus from --gpus or config and ignoring --tensor-parallel-size configured in serve vllm_args INFO 2025-06-19 06:42:06,115 instructlab.model.backends.vllm:115: Trying to connect to model server at http://127.0.0.1:8000/v1 INFO 2025-06-19 06:42:07,385 instructlab.model.backends.vllm:332: vLLM starting up on pid 12859 at http://127.0.0.1:44327/v1 INFO 2025-06-19 06:42:07,385 instructlab.model.backends.vllm:123: Starting a temporary vLLM server at http://127.0.0.1:44327/v1 INFO 2025-06-19 06:42:07,385 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 1/120 INFO 2025-06-19 06:42:10,865 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 2/120 INFO 06-19 06:42:12 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:42:13 [api_server.py:1034] vLLM API server version 0.8.4 INFO 06-19 06:42:13 [api_server.py:1035] args: Namespace(host='127.0.0.1', port=44327, uvicorn_log_level='info', disable_uvicorn_access_log=False, allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template='/tmp/tmp9hpyl1g7', chat_template_content_format='auto', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, enable_ssl_refresh=False, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_request_id_headers=False, enable_auto_tool_choice=False, tool_call_parser=None, tool_parser_plugin='', model='/var/home/rhcert/.cache/instructlab/models/prometheus-8x7b-v2-0', task='auto', tokenizer=None, hf_config_path=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, allowed_local_media_path=None, load_format='auto', download_dir=None, model_loader_extra_config=None, use_tqdm_on_load=True, config_format=, dtype='auto', kv_cache_dtype='auto', max_model_len=None, guided_decoding_backend='auto', logits_processor_pattern=None, model_impl='auto', distributed_executor_backend='mp', pipeline_parallel_size=1, tensor_parallel_size=8, data_parallel_size=1, enable_expert_parallel=False, max_parallel_loading_workers=None, ray_workers_use_nsight=False, disable_custom_all_reduce=False, block_size=None, enable_prefix_caching=None, prefix_caching_hash_algo='builtin', disable_sliding_window=False, use_v2_block_manager=True, num_lookahead_slots=0, seed=None, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_partial_prefills=1, max_long_partial_prefills=1, long_prefill_token_threshold=0, max_num_seqs=None, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, hf_token=None, hf_overrides=None, enforce_eager=False, max_seq_len_to_capture=8192, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, disable_mm_preprocessor_cache=False, enable_lora=False, enable_lora_bias=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=['prometheus-8x7b-v2-0'], qlora_adapter_name_or_path=None, show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, scheduling_policy='fcfs', scheduler_cls='vllm.core.scheduler.Scheduler', override_neuron_config=None, override_pooler_config=None, compilation_config=None, kv_transfer_config=None, worker_cls='auto', worker_extension_cls='', generation_config='auto', override_generation_config=None, enable_sleep_mode=False, calculate_kv_scales=False, additional_config=None, enable_reasoning=False, reasoning_parser=None, disable_cascade_attn=False, disable_chunked_mm_input=False, disable_log_requests=False, max_log_len=None, disable_fastapi_docs=False, enable_prompt_tokens_details=False, enable_server_load_tracking=False) INFO 2025-06-19 06:42:14,109 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 3/120 INFO 2025-06-19 06:42:17,426 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 4/120 INFO 2025-06-19 06:42:20,639 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 5/120 INFO 2025-06-19 06:42:23,896 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 6/120 INFO 2025-06-19 06:42:27,253 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 7/120 INFO 2025-06-19 06:42:30,600 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 8/120 INFO 2025-06-19 06:42:33,847 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 9/120 INFO 2025-06-19 06:42:37,176 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 10/120 INFO 2025-06-19 06:42:40,564 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 11/120 INFO 2025-06-19 06:42:43,936 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 12/120 INFO 2025-06-19 06:42:47,374 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 13/120 INFO 2025-06-19 06:42:50,809 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 14/120 INFO 2025-06-19 06:42:53,974 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 15/120 INFO 2025-06-19 06:42:57,185 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 16/120 INFO 2025-06-19 06:43:00,497 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 17/120 INFO 2025-06-19 06:43:03,826 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 18/120 INFO 2025-06-19 06:43:07,036 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 19/120 INFO 2025-06-19 06:43:10,421 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 20/120 INFO 2025-06-19 06:43:13,800 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 21/120 INFO 2025-06-19 06:43:17,105 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 22/120 INFO 2025-06-19 06:43:20,411 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 23/120 INFO 2025-06-19 06:43:23,561 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 24/120 INFO 2025-06-19 06:43:26,936 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 25/120 INFO 2025-06-19 06:43:30,343 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 26/120 INFO 06-19 06:43:31 [config.py:689] This model supports multiple tasks: {'embed', 'generate', 'classify', 'reward', 'score'}. Defaulting to 'generate'. INFO 06-19 06:43:31 [arg_utils.py:1742] rocm is experimental on VLLM_USE_V1=1. Falling back to V0 Engine. INFO 2025-06-19 06:43:33,647 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 27/120 INFO 2025-06-19 06:43:36,931 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 28/120 INFO 2025-06-19 06:43:40,348 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 29/120 INFO 2025-06-19 06:43:43,802 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 30/120 INFO 2025-06-19 06:43:47,152 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 31/120 INFO 2025-06-19 06:43:50,441 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 32/120 INFO 2025-06-19 06:43:53,656 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 33/120 INFO 2025-06-19 06:43:56,846 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 34/120 INFO 2025-06-19 06:44:00,152 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 35/120 INFO 2025-06-19 06:44:03,413 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 36/120 INFO 2025-06-19 06:44:06,656 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 37/120 INFO 2025-06-19 06:44:09,995 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 38/120 INFO 2025-06-19 06:44:13,323 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 39/120 INFO 2025-06-19 06:44:16,684 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 40/120 INFO 2025-06-19 06:44:19,967 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 41/120 INFO 2025-06-19 06:44:23,438 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 42/120 INFO 2025-06-19 06:44:26,746 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 43/120 INFO 06-19 06:44:29 [api_server.py:246] Started engine process with PID 12907 INFO 2025-06-19 06:44:29,948 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 44/120 INFO 2025-06-19 06:44:33,366 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 45/120 INFO 06-19 06:44:33 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:44:34 [llm_engine.py:243] Initializing a V0 LLM engine (v0.8.4) with config: model='/var/home/rhcert/.cache/instructlab/models/prometheus-8x7b-v2-0', speculative_config=None, tokenizer='/var/home/rhcert/.cache/instructlab/models/prometheus-8x7b-v2-0', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=8, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='auto', reasoning_backend=None), observability_config=ObservabilityConfig(show_hidden_metrics=False, otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=None, served_model_name=prometheus-8x7b-v2-0, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=None, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"max_capture_size":256}, use_cached_outputs=True, WARNING 06-19 06:44:34 [multiproc_worker_utils.py:306] Reducing Torch parallelism from 64 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed. INFO 2025-06-19 06:44:36,754 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 46/120 INFO 2025-06-19 06:44:40,064 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 47/120 INFO 2025-06-19 06:44:43,328 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 48/120 INFO 2025-06-19 06:44:46,668 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 49/120 INFO 2025-06-19 06:44:50,032 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 50/120 INFO 2025-06-19 06:44:53,239 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 51/120 INFO 2025-06-19 06:44:56,568 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 52/120 INFO 2025-06-19 06:44:59,932 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 53/120 INFO 2025-06-19 06:45:03,417 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 54/120 INFO 2025-06-19 06:45:06,777 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 55/120 INFO 06-19 06:45:06 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:45:07 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:45:09 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:45:09 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:45:09 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:45:09 [__init__.py:239] Automatically detected platform rocm. INFO 2025-06-19 06:45:10,072 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 56/120 INFO 06-19 06:45:11 [__init__.py:239] Automatically detected platform rocm. INFO 2025-06-19 06:45:13,327 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 57/120 (VllmWorkerProcess pid=12935) INFO 06-19 06:45:15 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=12931) INFO 06-19 06:45:16 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks INFO 2025-06-19 06:45:16,636 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 58/120 (VllmWorkerProcess pid=12933) INFO 06-19 06:45:17 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=12930) INFO 06-19 06:45:17 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=12932) INFO 06-19 06:45:17 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=12934) INFO 06-19 06:45:17 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks INFO 2025-06-19 06:45:19,998 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 59/120 (VllmWorkerProcess pid=12929) INFO 06-19 06:45:20 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks INFO 2025-06-19 06:45:23,234 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 60/120 INFO 2025-06-19 06:45:26,503 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 61/120 INFO 2025-06-19 06:45:29,688 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 62/120 INFO 2025-06-19 06:45:33,114 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 63/120 INFO 2025-06-19 06:45:36,588 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 64/120 INFO 2025-06-19 06:45:39,742 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 65/120 INFO 2025-06-19 06:45:43,050 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 66/120 INFO 2025-06-19 06:45:46,405 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 67/120 INFO 2025-06-19 06:45:49,774 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 68/120 INFO 2025-06-19 06:45:53,207 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 69/120 INFO 2025-06-19 06:45:56,636 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 70/120 INFO 2025-06-19 06:45:59,989 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 71/120 INFO 2025-06-19 06:46:03,228 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 72/120 INFO 2025-06-19 06:46:06,535 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 73/120 INFO 2025-06-19 06:46:09,865 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 74/120 INFO 2025-06-19 06:46:13,033 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 75/120 INFO 2025-06-19 06:46:16,378 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 76/120 INFO 2025-06-19 06:46:19,627 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 77/120 INFO 2025-06-19 06:46:22,953 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 78/120 INFO 2025-06-19 06:46:26,305 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 79/120 INFO 2025-06-19 06:46:29,650 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 80/120 INFO 2025-06-19 06:46:33,005 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 81/120 INFO 2025-06-19 06:46:36,384 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 82/120 INFO 2025-06-19 06:46:39,636 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 83/120 INFO 2025-06-19 06:46:42,867 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 84/120 INFO 2025-06-19 06:46:46,082 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 85/120 INFO 2025-06-19 06:46:49,429 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 86/120 INFO 2025-06-19 06:46:52,758 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 87/120 INFO 2025-06-19 06:46:56,005 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 88/120 INFO 2025-06-19 06:46:59,197 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 89/120 INFO 06-19 06:47:00 [rocm.py:153] None is not supported in AMD GPUs. INFO 06-19 06:47:00 [rocm.py:154] Using ROCmFlashAttention backend. INFO 2025-06-19 06:47:02,554 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 90/120 INFO 2025-06-19 06:47:06,014 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 91/120 INFO 2025-06-19 06:47:09,235 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 92/120 INFO 2025-06-19 06:47:12,450 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 93/120 INFO 2025-06-19 06:47:15,734 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 94/120 INFO 2025-06-19 06:47:19,147 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 95/120 INFO 2025-06-19 06:47:22,367 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 96/120 INFO 2025-06-19 06:47:25,603 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 97/120 INFO 2025-06-19 06:47:28,850 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 98/120 INFO 2025-06-19 06:47:32,184 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 99/120 INFO 2025-06-19 06:47:35,430 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 100/120 INFO 2025-06-19 06:47:38,781 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 101/120 INFO 2025-06-19 06:47:42,006 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 102/120 INFO 2025-06-19 06:47:45,226 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 103/120 INFO 2025-06-19 06:47:48,369 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 104/120 (VllmWorkerProcess pid=12935) INFO 06-19 06:47:51 [rocm.py:153] None is not supported in AMD GPUs. (VllmWorkerProcess pid=12935) INFO 06-19 06:47:51 [rocm.py:154] Using ROCmFlashAttention backend. INFO 2025-06-19 06:47:51,665 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 105/120 INFO 2025-06-19 06:47:54,883 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 106/120 INFO 2025-06-19 06:47:58,304 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 107/120 INFO 2025-06-19 06:48:01,551 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 108/120 INFO 2025-06-19 06:48:04,752 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 109/120 INFO 2025-06-19 06:48:08,061 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 110/120 INFO 2025-06-19 06:48:11,417 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 111/120 INFO 2025-06-19 06:48:14,776 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 112/120 INFO 2025-06-19 06:48:18,176 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 113/120 INFO 2025-06-19 06:48:21,588 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 114/120 INFO 2025-06-19 06:48:24,828 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 115/120 INFO 2025-06-19 06:48:28,191 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 116/120 INFO 2025-06-19 06:48:31,475 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 117/120 INFO 2025-06-19 06:48:34,929 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 118/120 INFO 2025-06-19 06:48:38,188 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 119/120 INFO 2025-06-19 06:48:41,391 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44327/v1, this might take a moment... Attempt: 120/120 INFO 2025-06-19 06:48:42,750 instructlab.model.backends.vllm:148: Gave up waiting for vLLM server to start at http://127.0.0.1:44327/v1 after 120 attempts Traceback (most recent call last): File "/usr/lib64/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete File "/opt/app-root/lib64/python3.11/site-packages/uvloop/__init__.py", line 61, in wrapper return await main ^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 1069, in run_server async with build_async_engine_client(args) as engine_client: File "/usr/lib64/python3.11/contextlib.py", line 210, in __aenter__ return await anext(self.gen) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 146, in build_async_engine_client async with build_async_engine_client_from_engine_args( File "/usr/lib64/python3.11/contextlib.py", line 210, in __aenter__ return await anext(self.gen) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 264, in build_async_engine_client_from_engine_args await mq_engine_client.setup() File "/opt/app-root/lib64/python3.11/site-packages/vllm/engine/multiprocessing/client.py", line 284, in setup response = await self._wait_for_server_rpc(socket) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/engine/multiprocessing/client.py", line 392, in _wait_for_server_rpc return await self._send_get_data_rpc_request( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/engine/multiprocessing/client.py", line 320, in _send_get_data_rpc_request if await socket.poll(timeout=VLLM_RPC_TIMEOUT) == 0: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ asyncio.exceptions.CancelledError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "", line 198, in _run_module_as_main File "", line 88, in _run_code File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 1121, in uvloop.run(run_server(args)) File "/opt/app-root/lib64/python3.11/site-packages/uvloop/__init__.py", line 105, in run return runner.run(wrapper()) ^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib64/python3.11/asyncio/runners.py", line 123, in run raise KeyboardInterrupt() KeyboardInterrupt INFO 2025-06-19 06:48:45,170 instructlab.model.backends.vllm:512: Waiting for GPU VRAM reclamation... INFO 2025-06-19 06:48:51,173 instructlab.model.backends.vllm:180: vLLM startup failed. Retrying (1/1) ERROR 2025-06-19 06:48:51,173 instructlab.model.backends.vllm:185: vLLM failed to start up in 395.4 seconds INFO 2025-06-19 06:48:51,174 instructlab.model.backends.vllm:115: Trying to connect to model server at http://127.0.0.1:8000/v1 INFO 2025-06-19 06:48:52,601 instructlab.model.backends.vllm:332: vLLM starting up on pid 12987 at http://127.0.0.1:44211/v1 INFO 2025-06-19 06:48:52,601 instructlab.model.backends.vllm:123: Starting a temporary vLLM server at http://127.0.0.1:44211/v1 INFO 2025-06-19 06:48:52,601 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 1/120 INFO 2025-06-19 06:48:56,070 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 2/120 INFO 06-19 06:48:56 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:48:58 [api_server.py:1034] vLLM API server version 0.8.4 INFO 06-19 06:48:58 [api_server.py:1035] args: Namespace(host='127.0.0.1', port=44211, uvicorn_log_level='info', disable_uvicorn_access_log=False, allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template='/tmp/tmp4n74hn2u', chat_template_content_format='auto', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, enable_ssl_refresh=False, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_request_id_headers=False, enable_auto_tool_choice=False, tool_call_parser=None, tool_parser_plugin='', model='/var/home/rhcert/.cache/instructlab/models/prometheus-8x7b-v2-0', task='auto', tokenizer=None, hf_config_path=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, allowed_local_media_path=None, load_format='auto', download_dir=None, model_loader_extra_config=None, use_tqdm_on_load=True, config_format=, dtype='auto', kv_cache_dtype='auto', max_model_len=None, guided_decoding_backend='auto', logits_processor_pattern=None, model_impl='auto', distributed_executor_backend='mp', pipeline_parallel_size=1, tensor_parallel_size=8, data_parallel_size=1, enable_expert_parallel=False, max_parallel_loading_workers=None, ray_workers_use_nsight=False, disable_custom_all_reduce=False, block_size=None, enable_prefix_caching=None, prefix_caching_hash_algo='builtin', disable_sliding_window=False, use_v2_block_manager=True, num_lookahead_slots=0, seed=None, swap_space=4, cpu_offload_gb=0, gpu_memory_utilization=0.9, num_gpu_blocks_override=None, max_num_batched_tokens=None, max_num_partial_prefills=1, max_long_partial_prefills=1, long_prefill_token_threshold=0, max_num_seqs=None, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, hf_token=None, hf_overrides=None, enforce_eager=False, max_seq_len_to_capture=8192, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config=None, limit_mm_per_prompt=None, mm_processor_kwargs=None, disable_mm_preprocessor_cache=False, enable_lora=False, enable_lora_bias=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=False, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', num_scheduler_steps=1, multi_step_stream_outputs=True, scheduler_delay_factor=0.0, enable_chunked_prefill=None, speculative_config=None, ignore_patterns=[], preemption_mode=None, served_model_name=['prometheus-8x7b-v2-0'], qlora_adapter_name_or_path=None, show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, scheduling_policy='fcfs', scheduler_cls='vllm.core.scheduler.Scheduler', override_neuron_config=None, override_pooler_config=None, compilation_config=None, kv_transfer_config=None, worker_cls='auto', worker_extension_cls='', generation_config='auto', override_generation_config=None, enable_sleep_mode=False, calculate_kv_scales=False, additional_config=None, enable_reasoning=False, reasoning_parser=None, disable_cascade_attn=False, disable_chunked_mm_input=False, disable_log_requests=False, max_log_len=None, disable_fastapi_docs=False, enable_prompt_tokens_details=False, enable_server_load_tracking=False) INFO 2025-06-19 06:48:59,514 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 3/120 INFO 2025-06-19 06:49:02,930 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 4/120 INFO 2025-06-19 06:49:06,304 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 5/120 INFO 2025-06-19 06:49:09,605 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 6/120 INFO 2025-06-19 06:49:12,806 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 7/120 INFO 2025-06-19 06:49:16,193 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 8/120 INFO 2025-06-19 06:49:19,390 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 9/120 INFO 2025-06-19 06:49:22,623 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 10/120 INFO 2025-06-19 06:49:25,965 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 11/120 INFO 2025-06-19 06:49:29,416 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 12/120 INFO 2025-06-19 06:49:32,876 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 13/120 INFO 2025-06-19 06:49:36,225 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 14/120 INFO 2025-06-19 06:49:39,603 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 15/120 INFO 2025-06-19 06:49:42,947 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 16/120 INFO 2025-06-19 06:49:46,256 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 17/120 INFO 2025-06-19 06:49:49,580 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 18/120 INFO 2025-06-19 06:49:52,829 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 19/120 INFO 2025-06-19 06:49:56,115 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 20/120 INFO 2025-06-19 06:49:59,295 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 21/120 INFO 2025-06-19 06:50:02,600 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 22/120 INFO 2025-06-19 06:50:05,973 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 23/120 INFO 2025-06-19 06:50:09,378 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 24/120 INFO 2025-06-19 06:50:12,645 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 25/120 INFO 2025-06-19 06:50:15,841 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 26/120 INFO 2025-06-19 06:50:19,170 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 27/120 INFO 2025-06-19 06:50:22,510 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 28/120 INFO 06-19 06:50:24 [config.py:689] This model supports multiple tasks: {'generate', 'reward', 'classify', 'embed', 'score'}. Defaulting to 'generate'. INFO 06-19 06:50:24 [arg_utils.py:1742] rocm is experimental on VLLM_USE_V1=1. Falling back to V0 Engine. INFO 2025-06-19 06:50:25,860 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 29/120 INFO 2025-06-19 06:50:29,148 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 30/120 INFO 2025-06-19 06:50:32,449 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 31/120 INFO 2025-06-19 06:50:35,910 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 32/120 INFO 2025-06-19 06:50:39,354 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 33/120 INFO 2025-06-19 06:50:42,726 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 34/120 INFO 2025-06-19 06:50:46,082 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 35/120 INFO 2025-06-19 06:50:49,460 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 36/120 INFO 2025-06-19 06:50:52,713 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 37/120 INFO 2025-06-19 06:50:56,119 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 38/120 INFO 2025-06-19 06:50:59,405 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 39/120 INFO 2025-06-19 06:51:02,741 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 40/120 INFO 2025-06-19 06:51:05,903 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 41/120 INFO 2025-06-19 06:51:09,331 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 42/120 INFO 2025-06-19 06:51:12,564 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 43/120 INFO 2025-06-19 06:51:15,944 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 44/120 INFO 2025-06-19 06:51:19,339 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 45/120 INFO 2025-06-19 06:51:22,755 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 46/120 INFO 2025-06-19 06:51:25,980 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 47/120 INFO 2025-06-19 06:51:29,256 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 48/120 INFO 2025-06-19 06:51:32,497 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 49/120 INFO 06-19 06:51:34 [api_server.py:246] Started engine process with PID 13035 INFO 2025-06-19 06:51:35,860 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 50/120 INFO 06-19 06:51:38 [__init__.py:239] Automatically detected platform rocm. INFO 2025-06-19 06:51:39,308 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 51/120 INFO 06-19 06:51:39 [llm_engine.py:243] Initializing a V0 LLM engine (v0.8.4) with config: model='/var/home/rhcert/.cache/instructlab/models/prometheus-8x7b-v2-0', speculative_config=None, tokenizer='/var/home/rhcert/.cache/instructlab/models/prometheus-8x7b-v2-0', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=8, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='auto', reasoning_backend=None), observability_config=ObservabilityConfig(show_hidden_metrics=False, otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=None, served_model_name=prometheus-8x7b-v2-0, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=None, chunked_prefill_enabled=False, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],"max_capture_size":256}, use_cached_outputs=True, WARNING 06-19 06:51:39 [multiproc_worker_utils.py:306] Reducing Torch parallelism from 64 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed. INFO 2025-06-19 06:51:42,606 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 52/120 INFO 2025-06-19 06:51:45,780 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 53/120 INFO 2025-06-19 06:51:49,213 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 54/120 INFO 2025-06-19 06:51:52,515 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 55/120 INFO 2025-06-19 06:51:55,882 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 56/120 INFO 2025-06-19 06:51:59,192 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 57/120 INFO 2025-06-19 06:52:02,436 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 58/120 INFO 2025-06-19 06:52:05,873 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 59/120 INFO 2025-06-19 06:52:09,168 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 60/120 INFO 2025-06-19 06:52:12,500 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 61/120 INFO 2025-06-19 06:52:15,897 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 62/120 INFO 06-19 06:52:16 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:52:18 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:52:18 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:52:18 [__init__.py:239] Automatically detected platform rocm. INFO 2025-06-19 06:52:19,128 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 63/120 INFO 06-19 06:52:19 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:52:19 [__init__.py:239] Automatically detected platform rocm. INFO 06-19 06:52:20 [__init__.py:239] Automatically detected platform rocm. INFO 2025-06-19 06:52:22,401 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 64/120 (VllmWorkerProcess pid=13058) INFO 06-19 06:52:25 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks INFO 2025-06-19 06:52:25,807 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 65/120 (VllmWorkerProcess pid=13062) INFO 06-19 06:52:26 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=13059) INFO 06-19 06:52:26 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=13061) INFO 06-19 06:52:26 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=13060) INFO 06-19 06:52:26 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=13063) INFO 06-19 06:52:26 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=13057) INFO 06-19 06:52:26 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks INFO 2025-06-19 06:52:29,208 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 66/120 INFO 2025-06-19 06:52:32,684 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 67/120 INFO 2025-06-19 06:52:35,984 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 68/120 INFO 2025-06-19 06:52:39,224 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 69/120 INFO 2025-06-19 06:52:42,615 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 70/120 INFO 2025-06-19 06:52:45,884 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 71/120 INFO 2025-06-19 06:52:49,193 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 72/120 INFO 2025-06-19 06:52:52,544 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 73/120 INFO 2025-06-19 06:52:55,841 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 74/120 INFO 2025-06-19 06:52:59,309 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 75/120 INFO 2025-06-19 06:53:02,668 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 76/120 INFO 2025-06-19 06:53:06,023 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 77/120 INFO 2025-06-19 06:53:09,241 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 78/120 INFO 2025-06-19 06:53:12,448 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 79/120 INFO 2025-06-19 06:53:15,906 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 80/120 INFO 2025-06-19 06:53:19,122 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 81/120 INFO 2025-06-19 06:53:22,358 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 82/120 INFO 2025-06-19 06:53:25,519 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 83/120 INFO 2025-06-19 06:53:28,854 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 84/120 INFO 2025-06-19 06:53:32,017 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 85/120 INFO 2025-06-19 06:53:35,388 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 86/120 INFO 2025-06-19 06:53:38,732 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 87/120 INFO 2025-06-19 06:53:42,023 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 88/120 INFO 2025-06-19 06:53:45,320 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 89/120 INFO 2025-06-19 06:53:48,586 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 90/120 INFO 2025-06-19 06:53:52,043 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 91/120 INFO 2025-06-19 06:53:55,426 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 92/120 INFO 2025-06-19 06:53:58,705 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 93/120 INFO 2025-06-19 06:54:01,974 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 94/120 INFO 2025-06-19 06:54:05,299 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 95/120 INFO 2025-06-19 06:54:08,772 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 96/120 INFO 2025-06-19 06:54:12,086 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 97/120 INFO 2025-06-19 06:54:15,290 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 98/120 INFO 2025-06-19 06:54:18,671 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 99/120 INFO 2025-06-19 06:54:21,991 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 100/120 INFO 2025-06-19 06:54:25,259 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 101/120 INFO 2025-06-19 06:54:28,563 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 102/120 INFO 2025-06-19 06:54:31,931 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 103/120 INFO 2025-06-19 06:54:35,390 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 104/120 INFO 2025-06-19 06:54:38,671 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 105/120 INFO 2025-06-19 06:54:41,943 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 106/120 INFO 2025-06-19 06:54:45,134 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 107/120 INFO 2025-06-19 06:54:48,491 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 108/120 INFO 2025-06-19 06:54:51,798 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 109/120 INFO 2025-06-19 06:54:55,114 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 110/120 INFO 2025-06-19 06:54:58,466 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 111/120 INFO 2025-06-19 06:55:01,726 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 112/120 INFO 2025-06-19 06:55:04,984 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 113/120 INFO 2025-06-19 06:55:08,408 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 114/120 INFO 2025-06-19 06:55:11,710 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 115/120 INFO 2025-06-19 06:55:15,110 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 116/120 INFO 2025-06-19 06:55:18,493 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 117/120 INFO 06-19 06:55:19 [rocm.py:153] None is not supported in AMD GPUs. INFO 06-19 06:55:19 [rocm.py:154] Using ROCmFlashAttention backend. INFO 2025-06-19 06:55:21,822 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 118/120 INFO 2025-06-19 06:55:25,170 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 119/120 INFO 2025-06-19 06:55:28,430 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:44211/v1, this might take a moment... Attempt: 120/120 INFO 2025-06-19 06:55:29,762 instructlab.model.backends.vllm:148: Gave up waiting for vLLM server to start at http://127.0.0.1:44211/v1 after 120 attempts Traceback (most recent call last): File "/usr/lib64/python3.11/asyncio/runners.py", line 118, in run return self._loop.run_until_complete(task) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "uvloop/loop.pyx", line 1518, in uvloop.loop.Loop.run_until_complete File "/opt/app-root/lib64/python3.11/site-packages/uvloop/__init__.py", line 61, in wrapper return await main ^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 1069, in run_server async with build_async_engine_client(args) as engine_client: File "/usr/lib64/python3.11/contextlib.py", line 210, in __aenter__ return await anext(self.gen) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 146, in build_async_engine_client async with build_async_engine_client_from_engine_args( File "/usr/lib64/python3.11/contextlib.py", line 210, in __aenter__ return await anext(self.gen) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 264, in build_async_engine_client_from_engine_args await mq_engine_client.setup() File "/opt/app-root/lib64/python3.11/site-packages/vllm/engine/multiprocessing/client.py", line 284, in setup response = await self._wait_for_server_rpc(socket) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/engine/multiprocessing/client.py", line 392, in _wait_for_server_rpc return await self._send_get_data_rpc_request( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/app-root/lib64/python3.11/site-packages/vllm/engine/multiprocessing/client.py", line 320, in _send_get_data_rpc_request if await socket.poll(timeout=VLLM_RPC_TIMEOUT) == 0: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ asyncio.exceptions.CancelledError During handling of the above exception, another exception occurred: Traceback (most recent call last): File "", line 198, in _run_module_as_main File "", line 88, in _run_code File "/opt/app-root/lib64/python3.11/site-packages/vllm/entrypoints/openai/api_server.py", line 1121, in uvloop.run(run_server(args)) File "/opt/app-root/lib64/python3.11/site-packages/uvloop/__init__.py", line 105, in run return runner.run(wrapper()) ^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib64/python3.11/asyncio/runners.py", line 123, in run raise KeyboardInterrupt() KeyboardInterrupt INFO 2025-06-19 06:55:32,934 instructlab.model.backends.vllm:512: Waiting for GPU VRAM reclamation... ERROR 2025-06-19 06:55:38,937 instructlab.model.evaluate:832: Failed to start server: vLLM failed to start up in 397.2 seconds ERROR 2025-06-19 06:55:38,937 instructlab.cli.model.evaluate:313: An error occurred during evaluation: Failed to start server: vLLM failed to start up in 397.2 seconds