Research expert Abhishek Bhandwaldar noted that the fine tuning phases should be running with different learning rates (2e-5 for phase 1 training and 6e-6 for phase 2 skills training) on granite 8b starter model in order to produce optimal results. Currently when you run ilab model train: you only get the option to specify one constant learning rate across the phases.

 

Expected behavior

• IBM Research expert Abhishek Bhandwaldar noted that the fine tuning phases should be running with different learning rates (2e-5 for phase 1 training and 6e-6 for phase 2 skills training) on granite 8b starter model in order to produce optimal results. The difference in expected results from running with a constant of 6e-6 across both phases is currently unknown 

Screenshots

• Attached Image 

Device Info (please complete the following information):

• Hardware Specs: 8x A100 machine IBM Cloud
• OS Version: RHEL AI 1.3
• InstructLab Version: 
  ilab, version 0.21.0

•  
• Provide the output of these two commands:
  • "registry.redhat.io/rhelai1/bootc-ibm-nvidia-rhel9:1.3"

Platform:

  sys.version: 3.11.7 (main, Oct  9 2024, 00:00:00) [GCC 11.4.1 20231218 (Red Hat 11.4.1-3)]

  sys.platform: linux

  os.name: posix

  platform.release: 5.14.0-427.42.1.el9_4.x86_64

  platform.machine: x86_64

  platform.node: tyler-machine-boot-6

  platform.python_version: 3.11.7

  os-release.ID: rhel

  os-release.VERSION_ID: 9.4

  os-release.PRETTY_NAME: Red Hat Enterprise Linux 9.4 (Plow)

  memory.total: 1259.87 GB

  memory.available: 1196.92 GB

  memory.used: 38.83 GB

 

InstructLab:

  instructlab.version: 0.21.0

  instructlab-dolomite.version: 0.2.0

  instructlab-eval.version: 0.4.1

  instructlab-quantize.version: 0.1.0

  instructlab-schema.version: 0.4.1

  instructlab-sdg.version: 0.6.1

  instructlab-training.version: 0.6.1

 

Torch:

  torch.version: 2.4.1

  torch.backends.cpu.capability: AVX512

  torch.version.cuda: 12.4

  torch.version.hip: None

  torch.cuda.available: True

  torch.backends.cuda.is_built: True

  torch.backends.mps.is_built: False

  torch.backends.mps.is_available: False

  torch.cuda.bf16: True

  torch.cuda.current.device: 0

  torch.cuda.0.name: NVIDIA A100-SXM4-80GB

  torch.cuda.0.free: 69.5 GB

  torch.cuda.0.total: 79.1 GB

  torch.cuda.0.capability: 8.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.1.name: NVIDIA A100-SXM4-80GB

  torch.cuda.1.free: 69.4 GB

  torch.cuda.1.total: 79.1 GB

  torch.cuda.1.capability: 8.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.2.name: NVIDIA A100-SXM4-80GB

  torch.cuda.2.free: 69.4 GB

  torch.cuda.2.total: 79.1 GB

  torch.cuda.2.capability: 8.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.3.name: NVIDIA A100-SXM4-80GB

  torch.cuda.3.free: 69.4 GB

  torch.cuda.3.total: 79.1 GB

  torch.cuda.3.capability: 8.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.4.name: NVIDIA A100-SXM4-80GB

  torch.cuda.4.free: 69.4 GB

  torch.cuda.4.total: 79.1 GB

  torch.cuda.4.capability: 8.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.5.name: NVIDIA A100-SXM4-80GB

  torch.cuda.5.free: 69.4 GB

  torch.cuda.5.total: 79.1 GB

  torch.cuda.5.capability: 8.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.6.name: NVIDIA A100-SXM4-80GB

  torch.cuda.6.free: 69.4 GB

  torch.cuda.6.total: 79.1 GB

  torch.cuda.6.capability: 8.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.7.name: NVIDIA A100-SXM4-80GB

  torch.cuda.7.free: 69.3 GB

  torch.cuda.7.total: 79.1 GB

  torch.cuda.7.capability: 8.0 (see https://developer.nvidia.com/cuda-gpus#compute)

 

llama_cpp_python:

  llama_cpp_python.version: 0.2.79

  llama_cpp_python.supports_gpu_offload: True

Additional context

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