amd / Llama-3.1-70B-Instruct-FP8-KV

Meta-Llama-3.1-70B-Instruct-FP8-KV

• Introduction

  This model was created by applying Quark with calibration samples from Pile dataset.

• Quantization Stragegy

  • Quantized Layers : All linear layers excluding "lm_head"
  • Weight : FP8 symmetric per-tensor
  • Activation : FP8 symmetric per-tensor
  • KV Cache : FP8 symmetric per-tensor

• Quick Start

1. Download and install Quark
2. Run the quantization script in the example folder using the following command line:

export MODEL_DIR = [local model checkpoint folder] or meta-llama/Meta-Llama-3.1-70B-Instruct 
# single GPU
python3 quantize_quark.py \ 
        --model_dir $MODEL_DIR \
        --output_dir Meta-Llama-3.1-70B-Instruct-FP8-KV \                           
        --quant_scheme w_fp8_a_fp8 \
        --kv_cache_dtype fp8 \
        --num_calib_data 128  \
        --model_export quark_safetensors \
        --no_weight_matrix_merge
# If model size is too large for single GPU, please use multi GPU instead.
python3 quantize_quark.py \ 
        --model_dir $MODEL_DIR \
        --output_dir Meta-Llama-3.1-70B-Instruct-FP8-KV \                           
        --quant_scheme w_fp8_a_fp8 \
        --kv_cache_dtype fp8 \
        --num_calib_data 128  \
        --model_export quark_safetensors \
        --no_weight_matrix_merge \
        --multi_gpu

Deployment

Quark has its own export format and allows FP8 quantized models to be efficiently deployed using the vLLM backend(vLLM-compatible).

Evaluation

Quark currently uses perplexity(PPL) as the evaluation metric for accuracy loss before and after quantization.The specific PPL algorithm can be referenced in the quantize_quark.py. The quantization evaluation results are conducted in pseudo-quantization mode, which may slightly differ from the actual quantized inference accuracy. These results are provided for reference only.

Evaluation scores

License

Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved.