README.md

---
license: llama3.1
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
pipeline_tag: text-generation
---

# EmbeddedLLM/Llama-3.1-8B-Instruct-w_fp8_per_channel_sym
- ## 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-channel
  - ***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
HIP_VISIBLE_DEVICES=0 python quantize_quark.py --model_dir $MODEL_DIR \
                          --output_dir /app/model/quark/Llama-3.1-8B-Instruct-w_fp8_per_channel_sym/ \
                          --quant_scheme w_fp8_per_channel_sym \
                          --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.
python quantize_quark.py --model_dir $MODEL_DIR \
      --output_dir /app/model/quark/Llama-3.1-8B-Instruct-w_fp8_per_channel_sym/ \
      --quant_scheme w_fp8_per_channel_sym \
      --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
<table>
  <tr>
   <td><strong>Benchmark</strong>
   </td>
   <td><strong>Meta-Llama-3.1-8B-Instruct </strong>
   </td>
   <td><strong>EmbeddedLLM/Llama-3.1-8B-Instruct-w_fp8_per_channel_sym(this model)</strong>
   </td>
  </tr>
  <tr>
   <td>Perplexity-wikitext2
   </td>
   <td>7.2169
   </td>
   <td>7.34375
   </td>
  </tr>
  
</table>