--- license: llama3.1 --- # Meta-Llama-3.1-8B-Instruct-FP8-KV - ## Introduction This model was created by applying [Quark](https://quark.docs.amd.com/latest/index.html) 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](https://quark.docs.amd.com/latest/install.html) 2. Run the quantization script in the example folder using the following command line: ```sh export MODEL_DIR = [local model checkpoint folder] or meta-llama/Meta-Llama-3.1-8B-Instruct # single GPU python3 quantize_quark.py \ --model_dir $MODEL_DIR \ --output_dir llama31_8b_amd \ --quant_scheme w_fp8_a_fp8 \ --kv_cache_dtype fp8 \ --num_calib_data 128 \ --model_export quark_safetensors # If model size is too large for single GPU, please use multi GPU instead. python3 quantize_quark.py \ --model_dir $MODEL_DIR \ --output_dir llama31_8b_amd \ --quant_scheme w_fp8_a_fp8 \ --kv_cache_dtype fp8 \ --num_calib_data 128 \ --multi_gpu \ --model_export quark_safetensors ``` ## 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
Benchmark | Meta-Llama-3.1-8B-Instruct | Meta-Llama-3.1-8B-Instruct-FP8-KV(this model) |
Perplexity-wikitext2 | 7.2169 | 7.2752 |