accelerated-peft-autogptq-foak-sample-configuration.yaml

# FMS Acceleration Plugin Configuration. 
#
# Each stanza incorporates various configurations for 
# different fine-tuning / training tasks.
plugins:
  # PEFT-related acceleration
  peft:

    # quantization-releated acceleration
    # e.g., kernels for quantized base weights
    quantization:

      # AutoGPTQ quantized base weights.
      auto_gptq:

        # Kernel to be used for GPTQ linear laeyer
        # NOTE: Not all kernels are suitable for PEFT training; need to use 
        # kernels that support autograd forward / backward. The best 
        # recommendation at the moment is "triton_v2".
        kernel: triton_v2

        # If true, then will already expect quantized checkpoint 
        # passed into TrainingArguments.model_name_or_path
        from_quantized: true

        # Setting to false, will create GPTQ-LORA using the local autogptq package.
        # if true, will create legacy implementation of GPTQ-LORA using external 
        # `auto_gptq`. Refer to README for installation instructions
        use_external_lib: false
      fused_ops_and_kernels:

        # load unsloth optimizations for these 4bit base layer weights.
        # currently only support "auto_gptq" and "bitsandbytes"
        base_layer: auto_gptq

        # activate various unsloth optimizations
        # there are two versions of the plugin
        # - the FastKernel version supports individual kernels
        # - the FastQuantized version is all-or-nothing


        # fused kernels for lora linear layers
        fused_lora: true

        # fast loss triton kernels
        fast_loss: true

        # fast rms norm triton kernels
        fast_rms_layernorm: true

        # fast RoPE embedding triton kernels
        fast_rope_embeddings: true