+ shuf -n 15000 /mnt/.local/share/instructlab/datasets/2025-05-19_170646/skills_train_msgs_2025-05-19T17_13_18.jsonl ++ ls -1 /mnt/.local/share/instructlab/datasets/ ++ head -n1 + tee iso-testrun/ilab-train ++ ls -1 /mnt/.local/share/instructlab/datasets/ ++ head -n1 ++ ls -1 /mnt/.local/share/instructlab/datasets/ ++ head -n1 + ilab model train -y --force-clear-phased-cache --enable-serving-output --strategy lab-multiphase --phased-phase1-data /mnt/.local/share/instructlab/datasets/2025-05-19_170646/knowledge_train_msgs_2025-05-19T17_13_18.jsonl --phased-phase2-data /mnt/.local/share/instructlab/datasets/2025-05-19_170646/skills_train_msgs_reduced.jsonl --phased-phase1-num-epochs 2 --phased-phase2-num-epochs 2 LoRA is disabled (rank=0), ignoring all additional LoRA args ~~~~~~~~~~~~~STARTING MULTI-PHASE TRAINING~~~~~~~~~~~~~ Running phased training with '2' epochs. Note: 7 epochs is the recommended amount for optimal performance. No training journal found. Will initialize at: '/mnt/.local/share/instructlab/phased/journalfile.yaml' Training Phase 1/2... TrainingArgs for current phase: TrainingArgs(model_path='/mnt/.cache/instructlab/models/granite-3.1-8b-starter-v2', chat_tmpl_path=None, use_legacy_tmpl=False, data_path='/mnt/.local/share/instructlab/datasets/2025-05-19_170646/knowledge_train_msgs_2025-05-19T17_13_18.jsonl', ckpt_output_dir='/mnt/.local/share/instructlab/phased/phase1/checkpoints', data_output_dir='/mnt/.local/share/instructlab/internal', max_seq_len=10000, max_batch_len=120000, num_epochs=2, effective_batch_size=128, save_samples=0, learning_rate=2e-05, warmup_steps=25, random_seed=42, use_dolomite=False, is_padding_free=False, checkpoint_at_epoch=True, accelerate_full_state_at_epoch=True, mock_data=False, mock_data_len=0, deepspeed_options=DeepSpeedOptions(cpu_offload_optimizer=False, cpu_offload_optimizer_ratio=1.0, cpu_offload_optimizer_pin_memory=False, save_samples=None), fsdp_options=FSDPOptions(cpu_offload_params=False, sharding_strategy=), distributed_backend=, disable_flash_attn=False, lora=LoraOptions(rank=0, alpha=32, dropout=0.1, target_modules=('q_proj', 'k_proj', 'v_proj', 'o_proj'), quantize_data_type=), process_data=True, keep_last_checkpoint_only=False, data_process_num_cpu_procs=16, use_liger=False) INFO 2025-05-19 17:48:02,036 numexpr.utils:146: Note: detected 208 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable. INFO 2025-05-19 17:48:02,036 numexpr.utils:149: Note: NumExpr detected 208 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16. INFO 2025-05-19 17:48:02,036 numexpr.utils:162: NumExpr defaulting to 16 threads. INFO 2025-05-19 17:48:18,060 datasets:54: PyTorch version 2.6.0 available. Generating train split: 5486 examples [00:00, 72690.93 examples/s] Ensuring dataset is compatible with legacy format. (num_proc=16): 100%|████████████████████████| 5486/5486 [00:00<00:00, 8004.19 examples/s] Converting samples into input_ids and labels... (num_proc=16): 100%|████████████████████████████| 5486/5486 [00:06<00:00, 877.80 examples/s] ten largest length percentiles: quantile 90th: 641.0 quantile 91th: 651.0 quantile 92th: 663.0 quantile 93th: 672.0 quantile 94th: 687.0 quantile 95th: 703.0 quantile 96th: 721.0 quantile 97th: 747.4499999999998 quantile 98th: 799.3000000000002 quantile 99th: 939.1499999999996 quantile 100th: 1398.0 at 10000 max sequence length, the number of samples to be dropped is 0 (0.00% of total) quantile 0th: 153.0 quantile 1th: 217.0 quantile 2th: 235.7 quantile 3th: 248.0 quantile 4th: 258.0 quantile 5th: 266.0 quantile 6th: 276.09999999999997 quantile 7th: 285.95000000000005 quantile 8th: 295.0 quantile 9th: 302.0 quantile 10th: 308.5 at 20 min sequence length, the number of samples to be dropped is 0 Filter (num_proc=16): 100%|████████████████████████████████████████████████████████████████████| 5486/5486 [00:02<00:00, 2406.71 examples/s] Samples Previews... Filtering out pretraining samples (num_proc=16): 100%|█████████████████████████████████████████| 5486/5486 [00:02<00:00, 2326.39 examples/s] Original Input: <|start_of_role|>system<|end_of_role|>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. Your primary role is to serve as a chat assistant.<|end_of_text|> <|start_of_role|>user<|end_of_role|>Swifties Swifties are the fandom of the American singer-songwriter Taylor Swift. Regarded by journalists as one of the largest, most devoted, and influential fan bases, Swifties are known for their high levels of participation, creativity, community, fanaticism, and cultural impact on the music industry and popular culture. They are a subject of widespread coverage in the mainstream media. Critics have opined that Swift has redefined artist–fandom relationships by establishing an intimate connection with Swifties. She has frequently engaged with, helped, credited and prioritized her fans, who have offered unprecedented support and interest in her works irrespective of her wavering reception in the media. They continued to support Swift through her genre transitions, unexpected artistic pivots, and her highly publicized controversies such as the 2019 masters dispute, while instigating the political scrutiny of Ticketmaster that led to implementation of various laws and stimulated economic growth with the Eras Tour. Swift's releases, promotional efforts, and fashion have garnered attention for incorporating Easter eggs and clues that are decoded by Swifties and considered part of her musical universe. Cultural analyses have variably described Swifties as a community of interest, a subculture, and a near-metaverse, while academics have studied them for their consumerism, content creation, social capital, collective effervescence, organizing prolificacy, and interpersonal relationships. The word Swiftie(s) was added to the Oxford Dictionary of English in 2023. Swifties have also been a subject of criticism, with some fans displaying disregard for Swift's privacy by publicizing her real-time locations and engaging in verbal attack of individuals, including celebrities, who malign Swift. On the other hand, some Swifties criticize Swift for her lifestyle and professional choices, which journalists disapprove as a parasocial relationship. How have Swifties been a subject of criticism?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>Swifties have been criticized for disregarding Taylor Swift's privacy, engaging in verbal attacks on individuals who malign Swift, and having parasocial relationships with Swift. <|end_of_text|> Pretraining ex sample 1: <|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Swifties Swifties are the fandom of the American singer-songwriter Taylor Swift. Regarded by journalists as one of the largest, most devoted, and influential fan bases, Swifties are known for their high levels of participation, creativity, community, fanaticism, and cultural impact on the music industry and popular culture. They are a subject of widespread coverage in the mainstream media. Critics have opined that Swift has redefined artist–fandom relationships by establishing an intimate connection with Swifties. She has frequently engaged with, helped, credited and prioritized her fans, who have offered unprecedented support and interest in her works irrespective of her wavering reception in the media. They continued to support Swift through her genre transitions, unexpected artistic pivots, and her highly publicized controversies such as the 2019 masters dispute, while instigating the political scrutiny of Ticketmaster that led to implementation of various laws and stimulated economic growth with the Eras Tour. Swift's releases, promotional efforts, and fashion have garnered attention for incorporating Easter eggs and clues that are decoded by Swifties and considered part of her musical universe. Cultural analyses have variably described Swifties as a community of interest, a subculture, and a near-metaverse, while academics have studied them for their consumerism, content creation, social capital, collective effervescence, organizing prolificacy, and interpersonal relationships. The word Swiftie(s) was added to the Oxford Dictionary of English in 2023. Swifties have also been a subject of criticism, with some fans displaying disregard for Swift's privacy by publicizing her real-time locations and engaging in verbal attack of individuals, including celebrities, who malign Swift. On the other hand, some Swifties criticize Swift for her lifestyle and professional choices, which journalists disapprove as a parasocial relationship. How have Swifties been a subject of criticism?<|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Swifties have been criticized for disregarding Taylor Swift's privacy, engaging in verbal attacks on individuals who malign Swift, and having parasocial relationships with Swift. <|end_of_text|><|MASK|> Original Input: <|start_of_role|>system<|end_of_role|>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. Your primary role is to serve as a chat assistant.<|end_of_text|> <|start_of_role|>user<|end_of_role|>Swifties History Etymology The word "Swiftie" for a Swift fan gained popularity in the late 2000s. Etymologically, the word is formed from Swift's name and the suffix "ie", which is often used in diminutives to imply affection. Swift stated in a 2012 Vevo interview that her fans call themselves "Swifties", which she found "adorable". Swift filed the term for trademark in March 2017. In 2023, Oxford Dictionary of English defined Swiftie as a noun meaning "an enthusiastic fan of the singer Taylor Swift." As per the dictionary, some words that collocate with Swiftie in popular usage are "fandom", "die-hard", "hardcore" and "self- proclaimed". According to Dictionary.com, the term Swiftie often implies that the person is "a very passionate and loyal fan—as opposed to just a casual listener." What is the significance of the term "Swiftie" implying a very passionate and loyal fan of Taylor Swift, as it relates to the behavior of Swifties?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>The term "Swiftie" implying a very passionate and loyal fan of Taylor Swift indicates the strong connection and devotion that her fans have towards her and her music, which could be seen as a driving force behind their enthusiastic and creative behavior towards her and her music. <|end_of_text|> Pretraining ex sample 2: <|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Swifties History Etymology The word "Swiftie" for a Swift fan gained popularity in the late 2000s. Etymologically, the word is formed from Swift's name and the suffix "ie", which is often used in diminutives to imply affection. Swift stated in a 2012 Vevo interview that her fans call themselves "Swifties", which she found "adorable". Swift filed the term for trademark in March 2017. In 2023, Oxford Dictionary of English defined Swiftie as a noun meaning "an enthusiastic fan of the singer Taylor Swift." As per the dictionary, some words that collocate with Swiftie in popular usage are "fandom", "die-hard", "hardcore" and "self- proclaimed". According to Dictionary.com, the term Swiftie often implies that the person is "a very passionate and loyal fan—as opposed to just a casual listener." What is the significance of the term "Swiftie" implying a very passionate and loyal fan of Taylor Swift, as it relates to the behavior of Swifties?<|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>The term "Swiftie" implying a very passionate and loyal fan of Taylor Swift indicates the strong connection and devotion that her fans have towards her and her music, which could be seen as a driving force behind their enthusiastic and creative behavior towards her and her music. <|end_of_text|><|MASK|> Original Input: <|start_of_role|>system<|end_of_role|>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. Your primary role is to serve as a chat assistant.<|end_of_text|> <|start_of_role|>user<|end_of_role|>The Black-capped chickadee starts forming breeding pairs in late fall and winter, with breeding occurring in the spring. These birds are mostly monogamous, but some males mate with multiple females. Females prefer dominant males, and their reproductive success is closely tied to the male's ranking. Nests are built in tree cavities 1-7 m above the ground, either excavated by the pair, naturally occurring, or reused woodpecker nests. Nest boxes are also used, with females choosing the site and both sexes excavating. The female builds the nest using coarse material like moss or bark, and finer material such as mammal hair. Nesting season is from late April to June, with higher-ranking females nesting first. Eggs are white with reddish-brown dots, averaging 1.52 cm x 1.22 cm in size, and there are six to eight eggs per clutch. Incubation lasts 11-14 days, performed by the female and fed by the male. If disturbed, the incubating female may hiss like a snake to deter predators. What is the role of the female's preference in the mating behavior of black-capped chickadees?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>Females prefer dominant males in black-capped chickadees, which influences their mating behavior. <|end_of_text|> Pretraining ex sample 3: <|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>The Black-capped chickadee starts forming breeding pairs in late fall and winter, with breeding occurring in the spring. These birds are mostly monogamous, but some males mate with multiple females. Females prefer dominant males, and their reproductive success is closely tied to the male's ranking. Nests are built in tree cavities 1-7 m above the ground, either excavated by the pair, naturally occurring, or reused woodpecker nests. Nest boxes are also used, with females choosing the site and both sexes excavating. The female builds the nest using coarse material like moss or bark, and finer material such as mammal hair. Nesting season is from late April to June, with higher-ranking females nesting first. Eggs are white with reddish-brown dots, averaging 1.52 cm x 1.22 cm in size, and there are six to eight eggs per clutch. Incubation lasts 11-14 days, performed by the female and fed by the male. If disturbed, the incubating female may hiss like a snake to deter predators. What is the role of the female's preference in the mating behavior of black-capped chickadees?<|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Females prefer dominant males in black-capped chickadees, which influences their mating behavior. <|end_of_text|><|MASK|> Filtering out pretraining samples (num_proc=16): 100%|█████████████████████████████████████████| 5486/5486 [00:02<00:00, 2342.51 examples/s] Validating unmask tokens not in data (num_proc=16): 100%|██████████████████████████████████████| 5486/5486 [00:02<00:00, 2261.35 examples/s] Creating json from Arrow format: 100%|████████████████████████████████████████████████████████████████████████| 6/6 [00:01<00:00, 5.00ba/s] Running training command as subprocess: torchrun --nnodes=1 --node_rank=0 --nproc_per_node=8 --rdzv_id=123 --rdzv_endpoint=127.0.0.1:12222 /opt/app-root/lib64/python3.11/site-packages/instructlab/training/main_ds.py --model_name_or_path=/mnt/.cache/instructlab/models/granite-3.1-8b-starter-v2 --data_path=/mnt/.local/share/instructlab/internal/data.jsonl --output_dir=/mnt/.local/share/instructlab/phased/phase1/checkpoints --num_epochs=2 --effective_batch_size=128 --learning_rate=2e-05 --num_warmup_steps=25 --save_samples=0 --log_level=INFO --max_batch_len=120000 --seed=42 --checkpoint_at_epoch --accelerate_full_state_at_epoch --lora_r=0 --lora_alpha=32 --lora_dropout=0.1 --lora_target_modules q_proj k_proj v_proj o_proj --distributed_training_framework=fsdp --fsdp_sharding_strategy=HYBRID_SHARD W0519 17:48:40.039000 636 torch/distributed/run.py:792] W0519 17:48:40.039000 636 torch/distributed/run.py:792] ***************************************** W0519 17:48:40.039000 636 torch/distributed/run.py:792] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0519 17:48:40.039000 636 torch/distributed/run.py:792] ***************************************** DeepSpeed CPU Optimizer is not available. Some features may be unavailable. DeepSpeed is not available. Some features may be unavailable. model_name_or_path: /mnt/.cache/instructlab/models/granite-3.1-8b-starter-v2 data_path: /mnt/.local/share/instructlab/internal/data.jsonl output_dir: /mnt/.local/share/instructlab/phased/phase1/checkpoints num_epochs: 2 current_epoch: 0 last_step: 0 effective_batch_size: 128 learning_rate: 2.0e-05 lr_scheduler: cosine num_warmup_steps: 25 save_samples: 0 save_samples_ds: null save_last: false checkpoint_at_epoch: true accelerate_full_state_at_epoch: true log_level: INFO seed: 42 mock_data: false mock_len: 2600 distributed_training_framework: fsdp fsdp_sharding_strategy: HYBRID_SHARD use_dolomite: false lora_r: 0 lora_alpha: 32 lora_dropout: 0.1 lora_quant_bits: null lora_target_modules: - q_proj - k_proj - v_proj - o_proj max_batch_len: 120000 cpu_offload_optimizer: false cpu_offload_params_fsdp: false cpu_offload_optimizer_pin_memory: false cpu_offload_optimizer_ratio: 1.0 NEFTune_alpha: null chat_tmpl_path: null disable_flash_attn: false keep_last_checkpoint_only: false use_liger: false { "script_params": { "model_name_or_path": "/mnt/.cache/instructlab/models/granite-3.1-8b-starter-v2", "data_path": "/mnt/.local/share/instructlab/internal/data.jsonl", "output_dir": "/mnt/.local/share/instructlab/phased/phase1/checkpoints", "num_epochs": 2, "current_epoch": 0, "last_step": 0, "effective_batch_size": 128, "learning_rate": 2e-05, "lr_scheduler": "cosine", "num_warmup_steps": 25, "save_samples": 0, "save_samples_ds": null, "save_last": false, "checkpoint_at_epoch": true, "accelerate_full_state_at_epoch": true, "log_level": "INFO", "seed": 42, "mock_data": false, "mock_len": 2600, "distributed_training_framework": "fsdp", "fsdp_sharding_strategy": "HYBRID_SHARD", "use_dolomite": false, "lora_r": 0, "lora_alpha": 32, "lora_dropout": 0.1, "lora_quant_bits": null, "lora_target_modules": [ "q_proj", "k_proj", "v_proj", "o_proj" ], "max_batch_len": 120000, "cpu_offload_optimizer": false, "cpu_offload_params_fsdp": false, "cpu_offload_optimizer_pin_memory": false, "cpu_offload_optimizer_ratio": 1.0, "NEFTune_alpha": null, "chat_tmpl_path": null, "disable_flash_attn": false, "keep_last_checkpoint_only": false, "use_liger": false }, "timestamp": "2025-05-19T17:50:10.828073" } Generating train split: 5486 examples [00:00, 12102.30 examples/s] { "num_gpus": 8, "avg_sample_len": 463.0094786729858, "effective_batch_size": 128, "max_batch_len_per_gpu": 120000, "packing_max_batch_len": 7408, "grad_accum": 1, "num_batches": 43, "avg_samples_per_batch": 127.5813953488372, "samples_per_gpu": 16, "total_samples": 5486, "timestamp": "2025-05-19T17:50:16.814040" } Loading checkpoint shards: 100%|██████████| 4/4 [00:33<00:00, 8.29s/it] Loading checkpoint shards: 100%|██████████| 4/4 [00:33<00:00, 8.29s/it] Loading checkpoint shards: 100%|██████████| 4/4 [00:33<00:00, 8.29s/it] Loading checkpoint shards: 100%|██████████| 4/4 [00:33<00:00, 8.29s/it] Loading checkpoint shards: 100%|██████████| 4/4 [00:33<00:00, 8.29s/it] Loading checkpoint shards: 100%|██████████| 4/4 [00:33<00:00, 8.29s/it] Loading checkpoint shards: 100%|██████████| 4/4 [00:33<00:00, 8.29s/it] Loading checkpoint shards: 100%|██████████| 4/4 [00:33<00:00, 8.29s/it] [17:50:50] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. [17:50:50] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. [17:50:50] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. [17:50:50] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. [17:50:50] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. [17:50:50] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. [17:50:50] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. [17:50:50] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. /opt/app-root/lib64/python3.11/site-packages/accelerate/accelerator.py:1731: UserWarning: Upcasted low precision parameters in GraniteForCausalLM because mixed precision turned on in FSDP. Affects: model.embed_tokens.weight, model.norm.weight. warnings.warn( /opt/app-root/lib64/python3.11/site-packages/accelerate/accelerator.py:1731: UserWarning: Upcasted low precision parameters in GraniteDecoderLayer because mixed precision turned on in FSDP. Affects: self_attn.q_proj.weight, self_attn.k_proj.weight, self_attn.v_proj.weight, self_attn.o_proj.weight, mlp.gate_proj.weight, mlp.up_proj.weight, mlp.down_proj.weight, input_layernorm.weight, post_attention_layernorm.weight. warnings.warn( /opt/app-root/lib64/python3.11/site-packages/accelerate/accelerator.py:1737: UserWarning: FSDP upcast of low precision parameters may affect the precision of model checkpoints. warnings.warn( Epoch 0: 0%| | 0/43 [00:00, train_1=TrainPhaseModel( started_at_utc=datetime.datetime(2025, 5, 19, 17, 47, 53, 426556, tzinfo=datetime.timezone.utc), ended_at_utc=datetime.datetime(2025, 5, 19, 18, 1, 17, 534095, tzinfo=datetime.timezone.utc), checkpoints=PosixPath('/mnt/.local/share/instructlab/phased/phase1/checkpoints') ), eval_1=None, train_2=None, eval_2=None, final_output=None ) Training Phase 2/2... TrainingArgs for current phase: TrainingArgs(model_path='/mnt/.local/share/instructlab/phased/phase1/checkpoints/hf_format/samples_10905', chat_tmpl_path=None, use_legacy_tmpl=False, data_path='/mnt/.local/share/instructlab/datasets/2025-05-19_170646/skills_train_msgs_reduced.jsonl', ckpt_output_dir='/mnt/.local/share/instructlab/phased/phase2/checkpoints', data_output_dir='/mnt/.local/share/instructlab/internal', max_seq_len=10000, max_batch_len=120000, num_epochs=2, effective_batch_size=3840, save_samples=0, learning_rate=6e-06, warmup_steps=25, random_seed=42, use_dolomite=False, is_padding_free=False, checkpoint_at_epoch=True, accelerate_full_state_at_epoch=True, mock_data=False, mock_data_len=0, deepspeed_options=DeepSpeedOptions(cpu_offload_optimizer=False, cpu_offload_optimizer_ratio=1.0, cpu_offload_optimizer_pin_memory=False, save_samples=None), fsdp_options=FSDPOptions(cpu_offload_params=False, sharding_strategy=), distributed_backend=, disable_flash_attn=False, lora=LoraOptions(rank=0, alpha=32, dropout=0.1, target_modules=('q_proj', 'k_proj', 'v_proj', 'o_proj'), quantize_data_type=), process_data=True, keep_last_checkpoint_only=False, data_process_num_cpu_procs=16, use_liger=False) Generating train split: 15000 examples [00:00, 19242.70 examples/s] Ensuring dataset is compatible with legacy format. (num_proc=16): 100%|██████████████████████| 15000/15000 [00:01<00:00, 8103.78 examples/s] Converting samples into input_ids and labels... (num_proc=16): 100%|██████████████████████████| 15000/15000 [00:54<00:00, 276.64 examples/s] ten largest length percentiles: quantile 90th: 2619.0 quantile 91th: 2873.09 quantile 92th: 3196.3199999999997 quantile 93th: 3594.0700000000015 quantile 94th: 3985.239999999998 quantile 95th: 4435.349999999995 quantile 96th: 5664.559999999987 quantile 97th: 8111.569999999978 quantile 98th: 13512.500000000033 quantile 99th: 28253.000000000022 quantile 100th: 185980.0 at 10000 max sequence length, the number of samples to be dropped is 383 (2.55% of total) quantile 0th: 74.0 quantile 1th: 172.0 quantile 2th: 197.0 quantile 3th: 216.0 quantile 4th: 234.96000000000004 quantile 5th: 253.0 quantile 6th: 273.93999999999994 quantile 7th: 291.0 quantile 8th: 308.0 quantile 9th: 324.0 quantile 10th: 339.0 at 20 min sequence length, the number of samples to be dropped is 0 Filter (num_proc=16): 100%|███████████████████████████████████████████████████████████████████| 15000/15000 [00:23<00:00, 647.40 examples/s] Samples Previews... Filtering out pretraining samples (num_proc=16): 100%|███████████████████████████████████████| 14617/14617 [00:13<00:00, 1091.43 examples/s] Original Input: <|start_of_role|>system<|end_of_role|>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. Your primary role is to serve as a chat assistant.<|end_of_text|> <|start_of_role|>user<|end_of_role|>The Swifties fan base is often compared to Beatlemania of the 20th century, such as by Jon Bream of Star Tribune. This is due to the intense frenzy, or "Swiftmania," surrounding Swift. Shows and television programs that feature Swift tend to see spikes in viewership because of the Swifties. For instance, Super Bowl LVIII had higher viewership due to Swift's connection to Kansas City Chiefs player Travis Kelce. Swift has been an influence to various music artists, including Olivia Rodrigo, Halsey, and Camila Cabello. Moreover, not only musicians but also other celebrities identify themselves as Swifties. What is the relationship between Swifties and Taylor Swift's music?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>Swifties are united by their love for Taylor Swift and her music. <|end_of_text|> Pretraining ex sample 1: <|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>The Swifties fan base is often compared to Beatlemania of the 20th century, such as by Jon Bream of Star Tribune. This is due to the intense frenzy, or "Swiftmania," surrounding Swift. Shows and television programs that feature Swift tend to see spikes in viewership because of the Swifties. For instance, Super Bowl LVIII had higher viewership due to Swift's connection to Kansas City Chiefs player Travis Kelce. Swift has been an influence to various music artists, including Olivia Rodrigo, Halsey, and Camila Cabello. Moreover, not only musicians but also other celebrities identify themselves as Swifties. What is the relationship between Swifties and Taylor Swift's music?<|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Swifties are united by their love for Taylor Swift and her music. <|end_of_text|><|MASK|> Original Input: <|start_of_role|>system<|end_of_role|>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. Your primary role is to serve as a chat assistant.<|end_of_text|> <|start_of_role|>user<|end_of_role|>1. The black-capped chickadee forms flocks during winter. 2. Dominance hierarchies are observed in these chickadee flocks. 3. Dominance hierarchies play a significant role in social behaviors among chickadees. 4. Chickadees with higher social rankings have better access to food during winter. 5. Higher social rank in chickadees leads to better body condition, increased territory size, and higher reproductive success. 6. Hierarchies among chickadees are linear and stable. 7. Once a relationship is established between two chickadees, it remains the same for many years. 8. Older and more experienced chickadees are usually dominant over younger ones. 9. Males are typically dominant over females in chickadees. 10. Dominant and subordinate chickadees differ in their foraging strategies. 11. Dominant chickadees control access to preferred resources. 12. Subordinate chickadees are restricted to foraging in novel, riskier, or suboptimal environments. 13. Subordinate chickadees are less cautious approaching novel foods and objects compared to dominant ones. 14. This behavior in subordinate chickadees is similar to subordinate primates. 15. Subordinate primates feed on novel food more readily than dominant individuals. 16. Subordinate primates are more used to eating suboptimal and unfamiliar food. 17. There is no difference in ability to learn novel foraging tasks between dominant and subordinate chickadees. How do dominance hierarchies affect access to food during winter for black capped chickadees?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>Chickadees with higher social rankings have better access to food during winter. <|end_of_text|> Pretraining ex sample 2: <|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>1. The black-capped chickadee forms flocks during winter. 2. Dominance hierarchies are observed in these chickadee flocks. 3. Dominance hierarchies play a significant role in social behaviors among chickadees. 4. Chickadees with higher social rankings have better access to food during winter. 5. Higher social rank in chickadees leads to better body condition, increased territory size, and higher reproductive success. 6. Hierarchies among chickadees are linear and stable. 7. Once a relationship is established between two chickadees, it remains the same for many years. 8. Older and more experienced chickadees are usually dominant over younger ones. 9. Males are typically dominant over females in chickadees. 10. Dominant and subordinate chickadees differ in their foraging strategies. 11. Dominant chickadees control access to preferred resources. 12. Subordinate chickadees are restricted to foraging in novel, riskier, or suboptimal environments. 13. Subordinate chickadees are less cautious approaching novel foods and objects compared to dominant ones. 14. This behavior in subordinate chickadees is similar to subordinate primates. 15. Subordinate primates feed on novel food more readily than dominant individuals. 16. Subordinate primates are more used to eating suboptimal and unfamiliar food. 17. There is no difference in ability to learn novel foraging tasks between dominant and subordinate chickadees. How do dominance hierarchies affect access to food during winter for black capped chickadees?<|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Chickadees with higher social rankings have better access to food during winter. <|end_of_text|><|MASK|> Original Input: <|start_of_role|>system<|end_of_role|>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. Your primary role is to serve as a chat assistant.<|end_of_text|> <|start_of_role|>user<|end_of_role|>Black-capped chickadee Behaviour and ecology Diet and foraging Insects (especially caterpillars) form a large part of their diet in summer. The birds hop along tree branches searching for food, sometimes hanging upside down or hovering; they may make short flights to catch insects in the air. Seeds and berries become more important in winter, though insect eggs and pupae are eaten when available. Black-capped chickadees have also been known to eat the fat off of dead mammals. Sunflower seeds are readily taken from bird feeders. The birds take a seed in their beak and commonly fly from the feeder to a tree, where they proceed to hammer the seed on a branch to open it. Like many other species in the family Paridae, black-capped chickadees commonly cache food, mostly seeds, but sometimes insects, also. Items are stored singly in various sites such as bark, dead leaves, clusters of conifer needles, or knothole. Memory for the location of caches can last up to 28 days. Within the first 24 hours, the birds can even remember the relative quality of the stored items. This caching behavoiur has led to black-capped chickadees having larger hippocampi compared to other chickadees, who themselves have relatively larger hippocampi compared to other caching birds in the Paridae family. This variation in size also exists within the black-capped chickadee population based on the region they inhabit, with those who live in harsher climates (such as Alaska) having larger hippocampi. However, no variation exists between the sexes. The size of the hippocampus within black-capped chickadees also varies throughout the year, being the largest in October, and the smallest in February. While the exact reason for this seasonal change is unknown, it is believed that the hippocampus grows to allow the chickadee to remember its cache locations, and then shrinks as those caches are used up. How does the size of the hippocampus in black-capped chickadees compare to that of other caching birds in the Paridae family?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>Black-capped chickadees have larger hippocampi compared to other caching birds in the Paridae family. <|end_of_text|> Pretraining ex sample 3: <|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Black-capped chickadee Behaviour and ecology Diet and foraging Insects (especially caterpillars) form a large part of their diet in summer. The birds hop along tree branches searching for food, sometimes hanging upside down or hovering; they may make short flights to catch insects in the air. Seeds and berries become more important in winter, though insect eggs and pupae are eaten when available. Black-capped chickadees have also been known to eat the fat off of dead mammals. Sunflower seeds are readily taken from bird feeders. The birds take a seed in their beak and commonly fly from the feeder to a tree, where they proceed to hammer the seed on a branch to open it. Like many other species in the family Paridae, black-capped chickadees commonly cache food, mostly seeds, but sometimes insects, also. Items are stored singly in various sites such as bark, dead leaves, clusters of conifer needles, or knothole. Memory for the location of caches can last up to 28 days. Within the first 24 hours, the birds can even remember the relative quality of the stored items. This caching behavoiur has led to black-capped chickadees having larger hippocampi compared to other chickadees, who themselves have relatively larger hippocampi compared to other caching birds in the Paridae family. This variation in size also exists within the black-capped chickadee population based on the region they inhabit, with those who live in harsher climates (such as Alaska) having larger hippocampi. However, no variation exists between the sexes. The size of the hippocampus within black-capped chickadees also varies throughout the year, being the largest in October, and the smallest in February. While the exact reason for this seasonal change is unknown, it is believed that the hippocampus grows to allow the chickadee to remember its cache locations, and then shrinks as those caches are used up. How does the size of the hippocampus in black-capped chickadees compare to that of other caching birds in the Paridae family?<|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Black-capped chickadees have larger hippocampi compared to other caching birds in the Paridae family. <|end_of_text|><|MASK|> Filtering out pretraining samples (num_proc=16): 100%|███████████████████████████████████████| 14617/14617 [00:13<00:00, 1086.26 examples/s] Original Input: <|start_of_role|>system<|end_of_role|>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. Your primary role is to serve as a chat assistant.<|end_of_text|> <|start_of_role|>user<|end_of_role|>Hi there, I see you're talking about localization and reflective subcategories. Can you explain what these are?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>Hi! Sure. Localization is a process in category theory where we formally add inverses to a certain class of morphisms in a category, and reflective subcategories are full subcategories closed under limits and containing an object for every object in the ambient category to which it is isomorphic via a morphism in the subcategory.<|end_of_text|> <|start_of_role|>user<|end_of_role|>I see. So what's the relationship between them?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>In general, localizations are not necessarily reflective, but if they are they have some nice properties. We can ask if a given localization is reflective, and if not, what its "quasireflective" localization is.<|end_of_text|> <|start_of_role|>user<|end_of_role|>What does it mean for a localization to be quasireflective?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>A quasireflective localization is a localization that satisfies a modified version of the universal property of a reflective localization. Instead of requiring all functors in the definition of a localization to be left adjoints, we require them to be left adjoints only for a certain class of morphisms.<|end_of_text|> <|start_of_role|>user<|end_of_role|>I see. So what are some natural examples of non-reflective localizations?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>One example is the derived category of a Grothendieck category, which is the localization of the category of chain complexes with respect to the class of quasi-isomorphisms. This localization is almost never reflective.<|end_of_text|> <|start_of_role|>user<|end_of_role|>And what about quasireflective localizations that are not reflective?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>As for quasireflective localizations that are not reflective, I don't have a specific example off the top of my head, but we can try to construct one using the concept of a concrete category. A concrete category is a category equipped with a faithful functor to the category of sets.<|end_of_text|> <|start_of_role|>user<|end_of_role|>I see. So what does it mean for a localization to be concrete?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>If a localization is concrete, then it is possible to represent its objects as sets and its morphisms as functions between sets. This is a very strong requirement on the class of morphisms we are inverting.<|end_of_text|> <|start_of_role|>user<|end_of_role|>I see. So what's the connection between quasireflective localizations and concrete categories?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>If a localization is quasireflective and the ambient category is concrete, then the localization is also concrete. However, the converse is not necessarily true. In other words, a localization can be concrete without being quasireflective.<|end_of_text|> <|start_of_role|>user<|end_of_role|>I see. So what's the main result about quasireflective localizations and concrete categories?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>The main result is that if a localization is quasireflective and the ambient category is concrete, then the localization is reflective. In other words, if a localization is quasireflective but not reflective, then the ambient category must not be concrete.<|end_of_text|> <|start_of_role|>user<|end_of_role|>I see. So what's the significance of this result?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>This result has implications for the study of model categories, which are categories equipped with a notion of weak equivalence. In particular, many natural model categories give rise to non-reflective localizations, which can be understood in terms of quasireflective localizations and concrete categories.<|end_of_text|> <|start_of_role|>user<|end_of_role|>I see. Thank you for explaining all this to me!<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>You're welcome! I'm glad I could help.<|end_of_text|> Instruction ex sample 1: <|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Hi! Sure. Localization is a process in category theory where we formally add inverses to a certain class of morphisms in a category, and reflective subcategories are full subcategories closed under limits and containing an object for every object in the ambient category to which it is isomorphic via a morphism in the subcategory.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>In general, localizations are not necessarily reflective, but if they are they have some nice properties. We can ask if a given localization is reflective, and if not, what its "quasireflective" localization is.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>A quasireflective localization is a localization that satisfies a modified version of the universal property of a reflective localization. Instead of requiring all functors in the definition of a localization to be left adjoints, we require them to be left adjoints only for a certain class of morphisms.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>One example is the derived category of a Grothendieck category, which is the localization of the category of chain complexes with respect to the class of quasi-isomorphisms. This localization is almost never reflective.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>As for quasireflective localizations that are not reflective, I don't have a specific example off the top of my head, but we can try to construct one using the concept of a concrete category. A concrete category is a category equipped with a faithful functor to the category of sets.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>If a localization is concrete, then it is possible to represent its objects as sets and its morphisms as functions between sets. This is a very strong requirement on the class of morphisms we are inverting.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>If a localization is quasireflective and the ambient category is concrete, then the localization is also concrete. However, the converse is not necessarily true. In other words, a localization can be concrete without being quasireflective.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>The main result is that if a localization is quasireflective and the ambient category is concrete, then the localization is reflective. In other words, if a localization is quasireflective but not reflective, then the ambient category must not be concrete.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>This result has implications for the study of model categories, which are categories equipped with a notion of weak equivalence. In particular, many natural model categories give rise to non-reflective localizations, which can be understood in terms of quasireflective localizations and concrete categories.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>You're welcome! I'm glad I could help.<|end_of_text|><|MASK|> Original Input: <|start_of_role|>system<|end_of_role|>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. Your primary role is to serve as a chat assistant.<|end_of_text|> <|start_of_role|>user<|end_of_role|>Představte si, že jste Oprah Winfrey, úspěšná mediální manažerka a filantropka. Jak se obvykle rozhodnete relaxovat a nabít síly ve svých vzácných volných chvílích? Máte ráda nějaké jednoduché každodenní aktivity, jako je příprava jídla pro své blízké, procházka v přírodě nebo cvičení pozornosti a meditace?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>Jako Oprah Winfrey, cením si svých momentů samoty a odpočinku uprostřed svého náročného rozvrhu. Následují některé způsoby, jakým se ráda obnovuji: 1. Čtení: Miluji se ponořit do dobré knihy, prozkoumávat různé žánry a učit se nové nápady. Je to skvělý způsob, jak rozšířit své znalosti a perspektivy. 2. Vaření: Nacházím velkou radost v tom, že vařím pro své blízké. Příprava jídla s láskou a péčí přináší teplo do mé duše a živiny těm, které mám ráda. 3. Meditace a pozornost: Praktikování pozornosti a meditace mi pomáhá zůstat uzemněná a středována. Často tyto praktiky začleňuji do svého každodenního rutiny, abych udržovala vnitřní klid a rovnováhu. 4. Procházky v přírodě: Miluji si procházky v přírodě, ať už je to místní park nebo malebná turistická stezka. Krása a klid přírody na mě působí uklidňujícím způsobem, který mi umožňuje znovu se spojit se sebou samým a světem kolem mě. 5. Trávení kvalitního času s přáteli a rodinou: Budování a udržování silných vztahů je pro mé blaho klíčové. Cením si času, který trávím se svými blízkými, zapojením se do hlubokých rozhovorů a sdílených zkušeností. 6. Jóga a cvičení: Zůstávat aktivní je pro mé fyzické a duševní zdraví klíčové. Ráda provozuji jógu, která kombinuje pozornost a pohyb, stejně jako jiné formy cvičení, abych udržovala své tělo silné a pružné. 7. Osobní reflexe a psaní deníku: Věnování času reflexi svého života, cílů a zkušeností mi pomáhá růst jako člověku. Psaní deníku mi umožňuje zpracovávat své myšlenky a emoce, poskytuje mi jasnost a směr. 8. Sledování filmů nebo televize: Oceňuji umění vyprávění příběhů prostřednictvím filmů a televizních pořadů. Je to skvělý únik, který může také inspirovat a vzdělávat. 9. Rituály péče o sebe: Prioritizace péče o sebe je klíčová pro mé celkové blaho. Ráda se oddávám aktivitám jako jsou relaxační koupele, masáže nebo praktikování rituálů péče o pleť, abych se rozmazlovala a obnovila. 10. Cestování a objevování: Objevování nových míst, kultur a nápadů je mé vášní. Ráda cestuji, abych rozšířila své obzory a získala nový pohled na život.<|end_of_text|> <|start_of_role|>user<|end_of_role|>Plánuji jít do zábavního parku tento léto. Chceš se přidat a užít si jízdy se mnou? Můžeme vykřikovat své plíce a strávit spolu zábavný den.<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>Jak to zní skvěle! Je v tom něco opravdu osvěžujícího, když se pustíte do toho a nechte se unášet vzrušením zábavního parku. Energie, smích, adrenalin z jízd – to je úžasný způsob, jak se odpojit od každodenních povinností a prostě se bavit. Ačkoli se nemohu připojit osobně, povzbuzuji vás, abyste šli a užili si svůj den v zábavním parku. Křičte na horských drahách, užijte si cukrovou vatu a vytvořte si vzpomínky, které vám budou přinášet úsměv na tvář po celá léta. Nezapomeňte se dostatečně zásobit tekutinami, použít ochranný krém a vzít si čas na odpočinek, abyste mohli užít celý den naplno. Užijte si to skvěle a nezapomeňte si udělat hodně fotek, abyste zachytili radost z tohoto dne!<|end_of_text|> <|start_of_role|>user<|end_of_role|>Měli jste kdy někdy pocit únavy ze svého života?<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>Určitě, i jako Oprah Winfrey jsem zažila okamžiky únavy a vyčerpání. Život se všemi svými požadavky a výzvami může někdy působit přetěžujícím způsobem, bez ohledu na to, kdo jste. Je důležité uznat tyto pocity a podniknout kroky k jejich řešení. Když se cítím unavená nebo přetížená, obracím se na svou podpůrnou síť, která zahrnuje blízké přátele, rodinu a mentory. Spoléhám se také na sebeobslužné praktiky, jako je meditace, psaní deníku a trávení času v přírodě, aby mi pomohly získat zpět rovnováhu a perspektivu. Je klíčové naslouchat svému tělu a mysli a dát si povolení odpočinout si a dobít energii, když je to potřeba. Život je maraton, ne sprint, a pečovat o sebe je klíčem k udržení energie a vášně na dlouhou trať. Pamatujte, že je v pořádku udělat krok zpět a praktikovat sebeuvědomění v těch okamžicích, kdy život působí zvláště namáhavě.<|end_of_text|> <|start_of_role|>user<|end_of_role|>Plánuji jít na bleší trh a najít nějaké skryté poklady. Chceš přijít a pomoci mi najít nějaké poklady? Můžeme mít spolu zábavný a vzrušující den.<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>Och, dobrodružství na bleších trzích zní jako skvělý nápad! Je v tom něco opravdu zvláštního, když procházíte myriadou předmětů, z nichž každý má svůj vlastní příběh a historii. Ačkoli se nemohu zúčastnit osobně, mohu se s vámi rozdělit o nadšení a nabídnout několik rad pro vaši hledání pokladů. Zde jsou některé tipy pro vaši výpravu na bleší trh: 1. **Přijďte brzo**: Ráno zalétlý ptáček chytá červa, nebo v tomto případě nejlepší poklady. Přijetí brzo vám dá první volbu předmětů a šanci na získání nejlepších nabídek před ostatními. 2. **Vytvořte seznam**: Přemýšlejte o tom, co doufáte, že najdete. Zda jde o vintage oblečení, antické nábytek nebo unikátní sběratelské předměty, seznam vám pomůže zůstat zaměřený. 3. **Oblečte se pohodlně**: Obuťte si pohodlné boty a oblečení. Pravděpodobně budete dělat hodně chůze a procházení předmětů, takže pohoda je klíčová. 4. **Moudře vyjednávat**: Nebojte se vyjednávat o cenách. Je to součást zábavy a kultury bleších trhů. Jen si pamatujte, abyste byli respektní a spravedliví ve svých nabídkách. 5. **Zkontrolujte předměty pečlivě**: Podívejte se na předměty, které vás zajímají. Zkontrolujte, zda není žádná poškození nebo opotřebení, která by mohla ovlivnit hodnotu nebo použití předmětu. 6. **Přinesli hotovost**: Mnoho prodejců na bleších trzích preferuje hotovostní transakce a někteří možná neakceptují karty. Mít hotovost na ruce usnadní proces nákupu. 7. **Zůstaňte hydratovaní a dělejte přestávky**: Přinesli vodu a občerstvení a nezapomeňte dělat přestávky. To vám pomůže zůstat energický a více si užívat zkušenosti. 8. **Buďte otevření překvapením**: Někdy jsou nejlepší nálezy ty, o kterých ani nevěděli, že je hledáte. Zůstaňte otevření mysli a nechte se přitahovat neočekávaným. 9. **Užijte si zkušenosti**: Kromě pokladů, které můžete najít, užijte si atmosféru, lidi a radost z lovu. Je to o zkušenosti tak jako o předmětech, které si domů vezmete. Mějte skvělý čas na bleších trzích a doufejme, že najdete nějaké skvělé poklady, které vám přinesou radost a nadšení!<|end_of_text|> Instruction ex sample 2: <|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Jako Oprah Winfrey, cením si svých momentů samoty a odpočinku uprostřed svého náročného rozvrhu. Následují některé způsoby, jakým se ráda obnovuji: 1. Čtení: Miluji se ponořit do dobré knihy, prozkoumávat různé žánry a učit se nové nápady. Je to skvělý způsob, jak rozšířit své znalosti a perspektivy. 2. Vaření: Nacházím velkou radost v tom, že vařím pro své blízké. Příprava jídla s láskou a péčí přináší teplo do mé duše a živiny těm, které mám ráda. 3. Meditace a pozornost: Praktikování pozornosti a meditace mi pomáhá zůstat uzemněná a středována. Často tyto praktiky začleňuji do svého každodenního rutiny, abych udržovala vnitřní klid a rovnováhu. 4. Procházky v přírodě: Miluji si procházky v přírodě, ať už je to místní park nebo malebná turistická stezka. Krása a klid přírody na mě působí uklidňujícím způsobem, který mi umožňuje znovu se spojit se sebou samým a světem kolem mě. 5. Trávení kvalitního času s přáteli a rodinou: Budování a udržování silných vztahů je pro mé blaho klíčové. Cením si času, který trávím se svými blízkými, zapojením se do hlubokých rozhovorů a sdílených zkušeností. 6. Jóga a cvičení: Zůstávat aktivní je pro mé fyzické a duševní zdraví klíčové. Ráda provozuji jógu, která kombinuje pozornost a pohyb, stejně jako jiné formy cvičení, abych udržovala své tělo silné a pružné. 7. Osobní reflexe a psaní deníku: Věnování času reflexi svého života, cílů a zkušeností mi pomáhá růst jako člověku. Psaní deníku mi umožňuje zpracovávat své myšlenky a emoce, poskytuje mi jasnost a směr. 8. Sledování filmů nebo televize: Oceňuji umění vyprávění příběhů prostřednictvím filmů a televizních pořadů. Je to skvělý únik, který může také inspirovat a vzdělávat. 9. Rituály péče o sebe: Prioritizace péče o sebe je klíčová pro mé celkové blaho. Ráda se oddávám aktivitám jako jsou relaxační koupele, masáže nebo praktikování rituálů péče o pleť, abych se rozmazlovala a obnovila. 10. Cestování a objevování: Objevování nových míst, kultur a nápadů je mé vášní. Ráda cestuji, abych rozšířila své obzory a získala nový pohled na život.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Jak to zní skvěle! Je v tom něco opravdu osvěžujícího, když se pustíte do toho a nechte se unášet vzrušením zábavního parku. Energie, smích, adrenalin z jízd – to je úžasný způsob, jak se odpojit od každodenních povinností a prostě se bavit. Ačkoli se nemohu připojit osobně, povzbuzuji vás, abyste šli a užili si svůj den v zábavním parku. Křičte na horských drahách, užijte si cukrovou vatu a vytvořte si vzpomínky, které vám budou přinášet úsměv na tvář po celá léta. Nezapomeňte se dostatečně zásobit tekutinami, použít ochranný krém a vzít si čas na odpočinek, abyste mohli užít celý den naplno. Užijte si to skvěle a nezapomeňte si udělat hodně fotek, abyste zachytili radost z tohoto dne!<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Určitě, i jako Oprah Winfrey jsem zažila okamžiky únavy a vyčerpání. Život se všemi svými požadavky a výzvami může někdy působit přetěžujícím způsobem, bez ohledu na to, kdo jste. Je důležité uznat tyto pocity a podniknout kroky k jejich řešení. Když se cítím unavená nebo přetížená, obracím se na svou podpůrnou síť, která zahrnuje blízké přátele, rodinu a mentory. Spoléhám se také na sebeobslužné praktiky, jako je meditace, psaní deníku a trávení času v přírodě, aby mi pomohly získat zpět rovnováhu a perspektivu. Je klíčové naslouchat svému tělu a mysli a dát si povolení odpočinout si a dobít energii, když je to potřeba. Život je maraton, ne sprint, a pečovat o sebe je klíčem k udržení energie a vášně na dlouhou trať. Pamatujte, že je v pořádku udělat krok zpět a praktikovat sebeuvědomění v těch okamžicích, kdy život působí zvláště namáhavě.<|end_of_text|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>Och, dobrodružství na bleších trzích zní jako skvělý nápad! Je v tom něco opravdu zvláštního, když procházíte myriadou předmětů, z nichž každý má svůj vlastní příběh a historii. Ačkoli se nemohu zúčastnit osobně, mohu se s vámi rozdělit o nadšení a nabídnout několik rad pro vaši hledání pokladů. Zde jsou některé tipy pro vaši výpravu na bleší trh: 1. **Přijďte brzo**: Ráno zalétlý ptáček chytá červa, nebo v tomto případě nejlepší poklady. Přijetí brzo vám dá první volbu předmětů a šanci na získání nejlepších nabídek před ostatními. 2. **Vytvořte seznam**: Přemýšlejte o tom, co doufáte, že najdete. Zda jde o vintage oblečení, antické nábytek nebo unikátní sběratelské předměty, seznam vám pomůže zůstat zaměřený. 3. **Oblečte se pohodlně**: Obuťte si pohodlné boty a oblečení. Pravděpodobně budete dělat hodně chůze a procházení předmětů, takže pohoda je klíčová. 4. **Moudře vyjednávat**: Nebojte se vyjednávat o cenách. Je to součást zábavy a kultury bleších trhů. Jen si pamatujte, abyste byli respektní a spravedliví ve svých nabídkách. 5. **Zkontrolujte předměty pečlivě**: Podívejte se na předměty, které vás zajímají. Zkontrolujte, zda není žádná poškození nebo opotřebení, která by mohla ovlivnit hodnotu nebo použití předmětu. 6. **Přinesli hotovost**: Mnoho prodejců na bleších trzích preferuje hotovostní transakce a někteří možná neakceptují karty. Mít hotovost na ruce usnadní proces nákupu. 7. **Zůstaňte hydratovaní a dělejte přestávky**: Přinesli vodu a občerstvení a nezapomeňte dělat přestávky. To vám pomůže zůstat energický a více si užívat zkušenosti. 8. **Buďte otevření překvapením**: Někdy jsou nejlepší nálezy ty, o kterých ani nevěděli, že je hledáte. Zůstaňte otevření mysli a nechte se přitahovat neočekávaným. 9. **Užijte si zkušenosti**: Kromě pokladů, které můžete najít, užijte si atmosféru, lidi a radost z lovu. Je to o zkušenosti tak jako o předmětech, které si domů vezmete. Mějte skvělý čas na bleších trzích a doufejme, že najdete nějaké skvělé poklady, které vám přinesou radost a nadšení!<|end_of_text|><|MASK|> Original Input: <|start_of_role|>system<|end_of_role|>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. Your primary role is to serve as a chat assistant.<|end_of_text|> <|start_of_role|>user<|end_of_role|>: You are tasked with creating a Discord bot command that returns the avatar of a specified user. The bot command should take a user mention as an argument and display the user's avatar in the channel. If no user is mentioned, the bot should display a help message explaining the usage of the command. Write a function `getAvatar` that takes in the `client`, `msg`, and `args` as parameters. Inside the function, use the Discord.js library to create an embed message with the title "Avatar Usage" and relevant information about the command. If no user mention is provided in the `args`, send the embed message to the channel. If a user is mentioned, retrieve the user's avatar URL and create another embed message with the user's avatar. Send this embed message to the channel.<|end_of_text|> <|start_of_role|>assistant<|end_of_role|>: ```javascript const Discord = require('discord.js'); exports.getAvatar = (client, msg, args) => { const avhelp = new Discord.MessageEmbed() .setTitle('Avatar Usage') .setAuthor('YourBotName') .setColor(0x32CD32) .addField('About', 'Returns the avatar of a specified user', false) .addField('Usage', 'y!avatar ', false) .addField('Perms required', 'None') .setThumbnail(client.user.avatarURL()) .setTimestamp(); const userr = args.join(' '); if (!userr) { return msg.channel.send({ embed: avhelp }); } const user = msg.mentions.users.first(); const avatar = user.avatarURL({ size: 2048 }); const avatarembed = new Discord.MessageEmbed() .setTitle(`Avatar of ${user.tag} (click here to get the avatar)`) .setURL(avatar) .setImage(avatar) .setTimestamp(); msg.channel.send({ embed: avatarembed }); }; ``` Note: Replace "YourBotName" with your bot's actual name in the `setAuthor` method.<|end_of_text|> Instruction ex sample 3: <|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|><|MASK|>: ```javascript const Discord = require('discord.js'); exports.getAvatar = (client, msg, args) => { const avhelp = new Discord.MessageEmbed() .setTitle('Avatar Usage') .setAuthor('YourBotName') .setColor(0x32CD32) .addField('About', 'Returns the avatar of a specified user', false) .addField('Usage', 'y!avatar ', false) .addField('Perms required', 'None') .setThumbnail(client.user.avatarURL()) .setTimestamp(); const userr = args.join(' '); if (!userr) { return msg.channel.send({ embed: avhelp }); } const user = msg.mentions.users.first(); const avatar = user.avatarURL({ size: 2048 }); const avatarembed = new Discord.MessageEmbed() .setTitle(`Avatar of ${user.tag} (click here to get the avatar)`) .setURL(avatar) .setImage(avatar) .setTimestamp(); msg.channel.send({ embed: avatarembed }); }; ``` Note: Replace "YourBotName" with your bot's actual name in the `setAuthor` method.<|end_of_text|><|MASK|> Validating unmask tokens not in data (num_proc=16): 100%|████████████████████████████████████| 14617/14617 [00:14<00:00, 1028.31 examples/s] Creating json from Arrow format: 100%|██████████████████████████████████████████████████████████████████████| 15/15 [00:01<00:00, 9.23ba/s] Running training command as subprocess: torchrun --nnodes=1 --node_rank=0 --nproc_per_node=8 --rdzv_id=123 --rdzv_endpoint=127.0.0.1:12222 /opt/app-root/lib64/python3.11/site-packages/instructlab/training/main_ds.py --model_name_or_path=/mnt/.local/share/instructlab/phased/phase1/checkpoints/hf_format/samples_10905 --data_path=/mnt/.local/share/instructlab/internal/data.jsonl --output_dir=/mnt/.local/share/instructlab/phased/phase2/checkpoints --num_epochs=2 --effective_batch_size=3840 --learning_rate=6e-06 --num_warmup_steps=25 --save_samples=0 --log_level=INFO --max_batch_len=120000 --seed=42 --checkpoint_at_epoch --accelerate_full_state_at_epoch --lora_r=0 --lora_alpha=32 --lora_dropout=0.1 --lora_target_modules q_proj k_proj v_proj o_proj --distributed_training_framework=fsdp --fsdp_sharding_strategy=HYBRID_SHARD W0519 18:03:25.347000 2266 torch/distributed/run.py:792] W0519 18:03:25.347000 2266 torch/distributed/run.py:792] ***************************************** W0519 18:03:25.347000 2266 torch/distributed/run.py:792] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. W0519 18:03:25.347000 2266 torch/distributed/run.py:792] ***************************************** DeepSpeed CPU Optimizer is not available. Some features may be unavailable. DeepSpeed is not available. Some features may be unavailable. model_name_or_path: /mnt/.local/share/instructlab/phased/phase1/checkpoints/hf_format/samples_10905 data_path: /mnt/.local/share/instructlab/internal/data.jsonl output_dir: /mnt/.local/share/instructlab/phased/phase2/checkpoints num_epochs: 2 current_epoch: 0 last_step: 0 effective_batch_size: 3840 learning_rate: 6.0e-06 lr_scheduler: cosine num_warmup_steps: 25 save_samples: 0 save_samples_ds: null save_last: false checkpoint_at_epoch: true accelerate_full_state_at_epoch: true log_level: INFO seed: 42 mock_data: false mock_len: 2600 distributed_training_framework: fsdp fsdp_sharding_strategy: HYBRID_SHARD use_dolomite: false lora_r: 0 lora_alpha: 32 lora_dropout: 0.1 lora_quant_bits: null lora_target_modules: - q_proj - k_proj - v_proj - o_proj max_batch_len: 120000 cpu_offload_optimizer: false cpu_offload_params_fsdp: false cpu_offload_optimizer_pin_memory: false cpu_offload_optimizer_ratio: 1.0 NEFTune_alpha: null chat_tmpl_path: null disable_flash_attn: false keep_last_checkpoint_only: false use_liger: false { "script_params": { "model_name_or_path": "/mnt/.local/share/instructlab/phased/phase1/checkpoints/hf_format/samples_10905", "data_path": "/mnt/.local/share/instructlab/internal/data.jsonl", "output_dir": "/mnt/.local/share/instructlab/phased/phase2/checkpoints", "num_epochs": 2, "current_epoch": 0, "last_step": 0, "effective_batch_size": 3840, "learning_rate": 6e-06, "lr_scheduler": "cosine", "num_warmup_steps": 25, "save_samples": 0, "save_samples_ds": null, "save_last": false, "checkpoint_at_epoch": true, "accelerate_full_state_at_epoch": true, "log_level": "INFO", "seed": 42, "mock_data": false, "mock_len": 2600, "distributed_training_framework": "fsdp", "fsdp_sharding_strategy": "HYBRID_SHARD", "use_dolomite": false, "lora_r": 0, "lora_alpha": 32, "lora_dropout": 0.1, "lora_quant_bits": null, "lora_target_modules": [ "q_proj", "k_proj", "v_proj", "o_proj" ], "max_batch_len": 120000, "cpu_offload_optimizer": false, "cpu_offload_params_fsdp": false, "cpu_offload_optimizer_pin_memory": false, "cpu_offload_optimizer_ratio": 1.0, "NEFTune_alpha": null, "chat_tmpl_path": null, "disable_flash_attn": false, "keep_last_checkpoint_only": false, "use_liger": false }, "timestamp": "2025-05-19T18:07:37.954487" } Generating train split: 14617 examples [00:02, 4996.85 examples/s] { "num_gpus": 8, "avg_sample_len": 1131.2743380994732, "effective_batch_size": 3840, "max_batch_len_per_gpu": 120000, "packing_max_batch_len": 108602, "grad_accum": 5, "num_batches": 19, "avg_samples_per_batch": 769.3157894736842, "samples_per_gpu": 96, "total_samples": 14617, "timestamp": "2025-05-19T18:07:46.158265" } Loading checkpoint shards: 100%|██████████| 7/7 [00:10<00:00, 1.47s/it] [18:07:56] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. Loading checkpoint shards: 100%|██████████| 7/7 [00:11<00:00, 1.59s/it] Loading checkpoint shards: 100%|██████████| 7/7 [00:11<00:00, 1.59s/it] [18:07:57] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. [18:07:57] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. Loading checkpoint shards: 100%|██████████| 7/7 [00:11<00:00, 1.62s/it] Loading checkpoint shards: 100%|██████████| 7/7 [00:11<00:00, 1.58s/it] [18:07:57] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. [18:07:57] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. Loading checkpoint shards: 100%|██████████| 7/7 [00:11<00:00, 1.64s/it] [18:07:57] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. Loading checkpoint shards: 100%|██████████| 7/7 [00:11<00:00, 1.67s/it] Loading checkpoint shards: 100%|██████████| 7/7 [00:11<00:00, 1.67s/it] [18:07:58] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. [18:07:58] WARNING sharding_strategy is deprecated in favor of reshard_after_forward. This will be removed in a future dataclasses.py:1839 version of Accelerate. /opt/app-root/lib64/python3.11/site-packages/accelerate/accelerator.py:1731: UserWarning: Upcasted low precision parameters in GraniteForCausalLM because mixed precision turned on in FSDP. Affects: model.embed_tokens.weight, model.norm.weight. warnings.warn( /opt/app-root/lib64/python3.11/site-packages/accelerate/accelerator.py:1731: UserWarning: Upcasted low precision parameters in GraniteDecoderLayer because mixed precision turned on in FSDP. Affects: self_attn.q_proj.weight, self_attn.k_proj.weight, self_attn.v_proj.weight, self_attn.o_proj.weight, mlp.gate_proj.weight, mlp.up_proj.weight, mlp.down_proj.weight, input_layernorm.weight, post_attention_layernorm.weight. warnings.warn( /opt/app-root/lib64/python3.11/site-packages/accelerate/accelerator.py:1737: UserWarning: FSDP upcast of low precision parameters may affect the precision of model checkpoints. warnings.warn( Epoch 0: 0%| | 0/19 [00:00, train_1=TrainPhaseModel( started_at_utc=datetime.datetime(2025, 5, 19, 17, 47, 53, 426556, tzinfo=datetime.timezone.utc), ended_at_utc=datetime.datetime(2025, 5, 19, 18, 1, 17, 534095, tzinfo=datetime.timezone.utc), checkpoints=PosixPath('/mnt/.local/share/instructlab/phased/phase1/checkpoints') ), eval_1=None, train_2=TrainPhaseModel( started_at_utc=datetime.datetime(2025, 5, 19, 18, 1, 17, 772171, tzinfo=datetime.timezone.utc), ended_at_utc=datetime.datetime(2025, 5, 19, 18, 32, 21, 817543, tzinfo=datetime.timezone.utc), checkpoints=PosixPath('/mnt/.local/share/instructlab/phased/phase2/checkpoints') ), eval_2=None, final_output=None ) MT-Bench evaluation for Phase 2... WARNING 2025-05-19 18:32:23,408 instructlab.model.evaluate:773: Using gpus from --gpus or config and ignoring --tensor-parallel-size configured in serve vllm_args INFO 2025-05-19 18:32:24,309 instructlab.model.backends.vllm:115: Trying to connect to model server at http://127.0.0.1:8000/v1 INFO 2025-05-19 18:32:25,757 instructlab.model.backends.vllm:332: vLLM starting up on pid 3131 at http://127.0.0.1:55513/v1 INFO 2025-05-19 18:32:25,757 instructlab.model.backends.vllm:123: Starting a temporary vLLM server at http://127.0.0.1:55513/v1 INFO 2025-05-19 18:32:25,757 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 1/120 INFO 2025-05-19 18:32:29,014 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 2/120 INFO 05-19 18:32:29 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:32:31 [api_server.py:1034] vLLM API server version 0.8.4 INFO 05-19 18:32:31 [api_server.py:1035] args: Namespace(host='127.0.0.1', port=55513, 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/tmpre4_2x2g', 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='/mnt/.local/share/instructlab/phased/phase2/checkpoints/hf_format/samples_29143', 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_29143'], 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 2025-05-19 18:32:32,506 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 3/120 INFO 2025-05-19 18:32:35,693 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 4/120 INFO 2025-05-19 18:32:38,986 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 5/120 INFO 2025-05-19 18:32:42,212 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 6/120 INFO 2025-05-19 18:32:45,384 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 7/120 INFO 2025-05-19 18:32:48,787 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 8/120 INFO 2025-05-19 18:32:52,047 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 9/120 INFO 2025-05-19 18:32:55,289 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 10/120 INFO 2025-05-19 18:32:58,746 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 11/120 INFO 2025-05-19 18:33:02,009 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 12/120 INFO 2025-05-19 18:33:05,367 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 13/120 INFO 2025-05-19 18:33:08,750 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 14/120 INFO 2025-05-19 18:33:11,931 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 15/120 INFO 2025-05-19 18:33:15,347 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 16/120 INFO 2025-05-19 18:33:18,645 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 17/120 INFO 2025-05-19 18:33:21,983 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 18/120 INFO 2025-05-19 18:33:25,257 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 19/120 INFO 2025-05-19 18:33:28,620 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 20/120 INFO 2025-05-19 18:33:31,940 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 21/120 INFO 05-19 18:33:34 [config.py:689] This model supports multiple tasks: {'generate', 'classify', 'reward', 'score', 'embed'}. Defaulting to 'generate'. INFO 05-19 18:33:34 [arg_utils.py:1742] rocm is experimental on VLLM_USE_V1=1. Falling back to V0 Engine. WARNING 05-19 18:33:34 [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 2025-05-19 18:33:35,205 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 22/120 INFO 2025-05-19 18:33:38,585 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 23/120 INFO 2025-05-19 18:33:41,965 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 24/120 INFO 2025-05-19 18:33:45,313 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 25/120 INFO 2025-05-19 18:33:48,777 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 26/120 INFO 2025-05-19 18:33:51,952 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 27/120 INFO 2025-05-19 18:33:55,125 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 28/120 INFO 2025-05-19 18:33:58,303 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 29/120 INFO 2025-05-19 18:34:01,608 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 30/120 INFO 2025-05-19 18:34:05,071 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 31/120 INFO 2025-05-19 18:34:08,438 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 32/120 INFO 2025-05-19 18:34:11,777 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 33/120 INFO 2025-05-19 18:34:15,151 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 34/120 INFO 2025-05-19 18:34:18,524 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 35/120 INFO 2025-05-19 18:34:21,809 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 36/120 INFO 2025-05-19 18:34:25,221 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 37/120 INFO 2025-05-19 18:34:28,398 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 38/120 INFO 2025-05-19 18:34:31,841 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 39/120 INFO 05-19 18:34:32 [api_server.py:246] Started engine process with PID 3179 INFO 2025-05-19 18:34:35,009 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 40/120 INFO 05-19 18:34:35 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:34:37 [llm_engine.py:243] Initializing a V0 LLM engine (v0.8.4) with config: model='/mnt/.local/share/instructlab/phased/phase2/checkpoints/hf_format/samples_29143', speculative_config=None, tokenizer='/mnt/.local/share/instructlab/phased/phase2/checkpoints/hf_format/samples_29143', 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_29143, 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 05-19 18:34:37 [multiproc_worker_utils.py:306] Reducing Torch parallelism from 104 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed. INFO 2025-05-19 18:34:38,246 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 41/120 INFO 2025-05-19 18:34:41,637 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 42/120 INFO 2025-05-19 18:34:44,967 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 43/120 INFO 2025-05-19 18:34:48,446 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 44/120 INFO 2025-05-19 18:34:51,791 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 45/120 INFO 2025-05-19 18:34:55,009 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 46/120 INFO 2025-05-19 18:34:58,340 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 47/120 INFO 2025-05-19 18:35:01,569 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 48/120 INFO 05-19 18:35:04 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:35:04 [__init__.py:239] Automatically detected platform rocm. INFO 2025-05-19 18:35:04,915 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 49/120 INFO 05-19 18:35:04 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:35:04 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:35:05 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:35:05 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:35:05 [__init__.py:239] Automatically detected platform rocm. INFO 2025-05-19 18:35:08,134 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 50/120 (VllmWorkerProcess pid=3204) INFO 05-19 18:35:11 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3207) INFO 05-19 18:35:11 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3206) INFO 05-19 18:35:11 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3203) INFO 05-19 18:35:11 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3205) INFO 05-19 18:35:11 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3202) INFO 05-19 18:35:11 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks INFO 2025-05-19 18:35:11,544 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 51/120 (VllmWorkerProcess pid=3201) INFO 05-19 18:35:11 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks INFO 2025-05-19 18:35:14,827 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 52/120 INFO 2025-05-19 18:35:18,175 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 53/120 INFO 2025-05-19 18:35:21,427 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 54/120 INFO 2025-05-19 18:35:24,712 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 55/120 INFO 2025-05-19 18:35:27,986 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 56/120 INFO 2025-05-19 18:35:31,207 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 57/120 INFO 2025-05-19 18:35:34,605 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 58/120 INFO 2025-05-19 18:35:37,950 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 59/120 INFO 2025-05-19 18:35:41,223 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 60/120 INFO 2025-05-19 18:35:44,689 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 61/120 INFO 2025-05-19 18:35:47,956 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 62/120 INFO 2025-05-19 18:35:51,332 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 63/120 INFO 2025-05-19 18:35:54,548 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 64/120 INFO 2025-05-19 18:35:57,850 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 65/120 INFO 2025-05-19 18:36:01,057 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 66/120 INFO 2025-05-19 18:36:04,493 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 67/120 INFO 2025-05-19 18:36:07,826 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 68/120 INFO 2025-05-19 18:36:11,116 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 69/120 INFO 2025-05-19 18:36:14,451 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 70/120 INFO 2025-05-19 18:36:17,680 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 71/120 INFO 2025-05-19 18:36:21,013 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 72/120 INFO 2025-05-19 18:36:24,349 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 73/120 INFO 2025-05-19 18:36:27,572 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 74/120 INFO 2025-05-19 18:36:30,946 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 75/120 INFO 2025-05-19 18:36:34,135 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 76/120 INFO 2025-05-19 18:36:37,482 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 77/120 INFO 2025-05-19 18:36:40,736 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 78/120 INFO 2025-05-19 18:36:44,029 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 79/120 INFO 2025-05-19 18:36:47,287 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 80/120 INFO 2025-05-19 18:36:50,518 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 81/120 INFO 2025-05-19 18:36:53,839 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 82/120 INFO 2025-05-19 18:36:57,130 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 83/120 INFO 2025-05-19 18:37:00,487 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 84/120 INFO 2025-05-19 18:37:03,677 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 85/120 INFO 2025-05-19 18:37:06,934 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 86/120 INFO 2025-05-19 18:37:10,287 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 87/120 INFO 2025-05-19 18:37:13,440 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 88/120 INFO 2025-05-19 18:37:16,710 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 89/120 INFO 2025-05-19 18:37:19,978 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 90/120 INFO 2025-05-19 18:37:23,213 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 91/120 INFO 2025-05-19 18:37:26,540 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 92/120 INFO 2025-05-19 18:37:29,832 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 93/120 INFO 2025-05-19 18:37:33,264 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 94/120 INFO 2025-05-19 18:37:36,631 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 95/120 INFO 2025-05-19 18:37:39,933 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 96/120 INFO 2025-05-19 18:37:43,320 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 97/120 INFO 2025-05-19 18:37:46,558 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 98/120 INFO 2025-05-19 18:37:49,827 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 99/120 INFO 2025-05-19 18:37:53,156 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 100/120 INFO 2025-05-19 18:37:56,438 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 101/120 INFO 2025-05-19 18:37:59,857 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 102/120 INFO 2025-05-19 18:38:03,031 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 103/120 INFO 2025-05-19 18:38:06,374 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 104/120 INFO 2025-05-19 18:38:09,596 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 105/120 INFO 2025-05-19 18:38:12,995 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 106/120 INFO 2025-05-19 18:38:16,366 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 107/120 INFO 2025-05-19 18:38:19,614 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 108/120 INFO 2025-05-19 18:38:23,012 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 109/120 INFO 2025-05-19 18:38:26,191 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 110/120 INFO 05-19 18:38:26 [rocm.py:153] None is not supported in AMD GPUs. INFO 05-19 18:38:26 [rocm.py:154] Using ROCmFlashAttention backend. INFO 2025-05-19 18:38:29,529 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 111/120 INFO 2025-05-19 18:38:32,876 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 112/120 INFO 2025-05-19 18:38:36,185 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 113/120 INFO 2025-05-19 18:38:39,422 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 114/120 INFO 2025-05-19 18:38:42,643 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 115/120 INFO 2025-05-19 18:38:45,938 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 116/120 INFO 2025-05-19 18:38:49,332 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 117/120 INFO 2025-05-19 18:38:52,633 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 118/120 INFO 2025-05-19 18:38:55,899 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 119/120 INFO 2025-05-19 18:38:59,334 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:55513/v1, this might take a moment... Attempt: 120/120 INFO 2025-05-19 18:39:00,583 instructlab.model.backends.vllm:148: Gave up waiting for vLLM server to start at http://127.0.0.1:55513/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 2025-05-19 18:39:05,253 instructlab.model.backends.vllm:512: Waiting for GPU VRAM reclamation... INFO 2025-05-19 18:39:11,254 instructlab.model.backends.vllm:180: vLLM startup failed. Retrying (1/1) ERROR 2025-05-19 18:39:11,255 instructlab.model.backends.vllm:185: vLLM failed to start up in 394.8 seconds INFO 2025-05-19 18:39:11,255 instructlab.model.backends.vllm:115: Trying to connect to model server at http://127.0.0.1:8000/v1 INFO 2025-05-19 18:39:12,594 instructlab.model.backends.vllm:332: vLLM starting up on pid 3253 at http://127.0.0.1:54461/v1 INFO 2025-05-19 18:39:12,594 instructlab.model.backends.vllm:123: Starting a temporary vLLM server at http://127.0.0.1:54461/v1 INFO 2025-05-19 18:39:12,594 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 1/120 INFO 2025-05-19 18:39:15,940 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 2/120 INFO 05-19 18:39:16 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:39:17 [api_server.py:1034] vLLM API server version 0.8.4 INFO 05-19 18:39:17 [api_server.py:1035] args: Namespace(host='127.0.0.1', port=54461, 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/tmphyi4tt19', 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='/mnt/.local/share/instructlab/phased/phase2/checkpoints/hf_format/samples_29143', 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_29143'], 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 2025-05-19 18:39:19,344 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 3/120 INFO 2025-05-19 18:39:22,745 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 4/120 INFO 2025-05-19 18:39:26,013 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 5/120 INFO 2025-05-19 18:39:29,355 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 6/120 INFO 2025-05-19 18:39:32,500 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 7/120 INFO 2025-05-19 18:39:35,878 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 8/120 INFO 2025-05-19 18:39:39,055 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 9/120 INFO 2025-05-19 18:39:42,390 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 10/120 INFO 2025-05-19 18:39:45,849 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 11/120 INFO 2025-05-19 18:39:49,230 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 12/120 INFO 2025-05-19 18:39:52,598 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 13/120 INFO 2025-05-19 18:39:55,976 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 14/120 INFO 2025-05-19 18:39:59,433 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 15/120 INFO 2025-05-19 18:40:02,772 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 16/120 INFO 2025-05-19 18:40:06,012 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 17/120 INFO 2025-05-19 18:40:09,487 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 18/120 INFO 2025-05-19 18:40:12,846 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 19/120 INFO 2025-05-19 18:40:16,279 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 20/120 INFO 2025-05-19 18:40:19,557 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 21/120 INFO 2025-05-19 18:40:22,871 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 22/120 INFO 2025-05-19 18:40:26,258 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 23/120 INFO 2025-05-19 18:40:29,489 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 24/120 INFO 2025-05-19 18:40:32,945 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 25/120 INFO 2025-05-19 18:40:36,230 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 26/120 INFO 05-19 18:40:39 [config.py:689] This model supports multiple tasks: {'classify', 'embed', 'generate', 'reward', 'score'}. Defaulting to 'generate'. INFO 05-19 18:40:39 [arg_utils.py:1742] rocm is experimental on VLLM_USE_V1=1. Falling back to V0 Engine. WARNING 05-19 18:40:39 [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 2025-05-19 18:40:39,604 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 27/120 INFO 2025-05-19 18:40:42,974 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 28/120 INFO 2025-05-19 18:40:46,277 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 29/120 INFO 2025-05-19 18:40:49,479 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 30/120 INFO 2025-05-19 18:40:52,856 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 31/120 INFO 2025-05-19 18:40:56,007 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 32/120 INFO 2025-05-19 18:40:59,355 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 33/120 INFO 2025-05-19 18:41:02,706 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 34/120 INFO 2025-05-19 18:41:06,021 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 35/120 INFO 2025-05-19 18:41:09,258 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 36/120 INFO 2025-05-19 18:41:12,535 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 37/120 INFO 2025-05-19 18:41:15,759 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 38/120 INFO 2025-05-19 18:41:19,154 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 39/120 INFO 2025-05-19 18:41:22,470 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 40/120 INFO 2025-05-19 18:41:25,752 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 41/120 INFO 2025-05-19 18:41:29,195 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 42/120 INFO 2025-05-19 18:41:32,536 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 43/120 INFO 2025-05-19 18:41:35,787 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 44/120 INFO 2025-05-19 18:41:39,175 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 45/120 INFO 2025-05-19 18:41:42,512 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 46/120 INFO 2025-05-19 18:41:45,647 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 47/120 INFO 2025-05-19 18:41:48,904 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 48/120 INFO 2025-05-19 18:41:52,148 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 49/120 INFO 2025-05-19 18:41:55,279 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 50/120 INFO 05-19 18:41:55 [api_server.py:246] Started engine process with PID 3301 INFO 2025-05-19 18:41:58,671 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 51/120 INFO 05-19 18:41:59 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:42:00 [llm_engine.py:243] Initializing a V0 LLM engine (v0.8.4) with config: model='/mnt/.local/share/instructlab/phased/phase2/checkpoints/hf_format/samples_29143', speculative_config=None, tokenizer='/mnt/.local/share/instructlab/phased/phase2/checkpoints/hf_format/samples_29143', 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_29143, 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 05-19 18:42:00 [multiproc_worker_utils.py:306] Reducing Torch parallelism from 104 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed. INFO 2025-05-19 18:42:02,017 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 52/120 INFO 2025-05-19 18:42:05,303 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 53/120 INFO 2025-05-19 18:42:08,653 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 54/120 INFO 2025-05-19 18:42:12,015 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 55/120 INFO 2025-05-19 18:42:15,192 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 56/120 INFO 2025-05-19 18:42:18,465 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 57/120 INFO 2025-05-19 18:42:21,833 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 58/120 INFO 2025-05-19 18:42:25,222 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 59/120 INFO 2025-05-19 18:42:28,471 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 60/120 INFO 2025-05-19 18:42:31,881 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 61/120 INFO 2025-05-19 18:42:35,162 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 62/120 INFO 2025-05-19 18:42:38,387 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 63/120 INFO 05-19 18:42:38 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:42:38 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:42:38 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:42:39 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:42:39 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:42:40 [__init__.py:239] Automatically detected platform rocm. INFO 05-19 18:42:41 [__init__.py:239] Automatically detected platform rocm. INFO 2025-05-19 18:42:41,638 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 64/120 INFO 2025-05-19 18:42:44,952 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 65/120 (VllmWorkerProcess pid=3326) INFO 05-19 18:42:46 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3327) INFO 05-19 18:42:46 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3324) INFO 05-19 18:42:46 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3325) INFO 05-19 18:42:47 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3329) INFO 05-19 18:42:47 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3323) INFO 05-19 18:42:47 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks (VllmWorkerProcess pid=3328) INFO 05-19 18:42:47 [multiproc_worker_utils.py:225] Worker ready; awaiting tasks INFO 2025-05-19 18:42:48,209 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 66/120 INFO 2025-05-19 18:42:51,587 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 67/120 INFO 2025-05-19 18:42:55,056 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 68/120 INFO 2025-05-19 18:42:58,446 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 69/120 INFO 2025-05-19 18:43:01,675 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 70/120 INFO 2025-05-19 18:43:05,033 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 71/120 INFO 2025-05-19 18:43:08,450 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 72/120 INFO 2025-05-19 18:43:11,788 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 73/120 INFO 2025-05-19 18:43:15,057 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 74/120 INFO 2025-05-19 18:43:18,548 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 75/120 INFO 2025-05-19 18:43:21,794 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 76/120 INFO 2025-05-19 18:43:25,056 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 77/120 INFO 2025-05-19 18:43:28,214 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 78/120 INFO 2025-05-19 18:43:31,561 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 79/120 INFO 2025-05-19 18:43:34,731 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 80/120 INFO 2025-05-19 18:43:38,077 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 81/120 INFO 2025-05-19 18:43:41,406 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 82/120 INFO 2025-05-19 18:43:44,895 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 83/120 INFO 2025-05-19 18:43:48,238 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 84/120 INFO 2025-05-19 18:43:51,567 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 85/120 INFO 2025-05-19 18:43:54,938 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 86/120 INFO 2025-05-19 18:43:58,272 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 87/120 INFO 2025-05-19 18:44:01,642 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 88/120 INFO 2025-05-19 18:44:04,877 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 89/120 INFO 2025-05-19 18:44:08,296 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 90/120 INFO 2025-05-19 18:44:11,586 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 91/120 INFO 2025-05-19 18:44:14,947 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 92/120 INFO 2025-05-19 18:44:18,285 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 93/120 INFO 2025-05-19 18:44:21,599 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 94/120 INFO 2025-05-19 18:44:25,047 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 95/120 INFO 2025-05-19 18:44:28,387 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 96/120 INFO 2025-05-19 18:44:31,770 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 97/120 INFO 2025-05-19 18:44:35,156 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 98/120 INFO 2025-05-19 18:44:38,563 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 99/120 INFO 2025-05-19 18:44:41,942 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 100/120 INFO 2025-05-19 18:44:45,161 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 101/120 INFO 2025-05-19 18:44:48,458 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 102/120 INFO 2025-05-19 18:44:51,939 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 103/120 INFO 2025-05-19 18:44:55,333 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 104/120 INFO 2025-05-19 18:44:58,726 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 105/120 INFO 2025-05-19 18:45:02,081 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 106/120 INFO 2025-05-19 18:45:05,420 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 107/120 INFO 2025-05-19 18:45:08,764 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 108/120 INFO 2025-05-19 18:45:11,978 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 109/120 INFO 2025-05-19 18:45:15,374 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 110/120 INFO 2025-05-19 18:45:18,738 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 111/120 INFO 2025-05-19 18:45:22,100 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 112/120 INFO 2025-05-19 18:45:25,318 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 113/120 INFO 2025-05-19 18:45:28,685 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 114/120 INFO 2025-05-19 18:45:32,010 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 115/120 INFO 2025-05-19 18:45:35,444 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 116/120 INFO 2025-05-19 18:45:38,744 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 117/120 INFO 2025-05-19 18:45:42,146 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 118/120 INFO 2025-05-19 18:45:45,401 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 119/120 INFO 2025-05-19 18:45:48,725 instructlab.model.backends.vllm:138: Waiting for the vLLM server to start at http://127.0.0.1:54461/v1, this might take a moment... Attempt: 120/120 INFO 2025-05-19 18:45:50,065 instructlab.model.backends.vllm:148: Gave up waiting for vLLM server to start at http://127.0.0.1:54461/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 2025-05-19 18:45:52,883 instructlab.model.backends.vllm:512: Waiting for GPU VRAM reclamation... ERROR 2025-05-19 18:45:58,885 instructlab.model.evaluate:832: Failed to start server: vLLM failed to start up in 397.5 seconds Accelerated Training failed with Failed to start server: vLLM failed to start up in 397.5 seconds real 58m12.297s user 0m1.926s sys 0m1.239s