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README.md

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+---
+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>
+