Create README.md

README.md

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+---
+language:
+- en
+pipeline_tag: text-generation
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
+---
+
+# DeepSeek-R1-Distill-Llama-8B-awq-asym-uint4-g128-lmhead-onnx-cpu
+- ## Introduction
+  This model was created by applying [Quark](https://quark.docs.amd.com/latest/index.html) with calibration samples from Pile dataset.
+- ## Quantization Strategy
+  - ***Quantized Layers***: All linear layers
+  - ***Weight***: uint4 asymmetric per-group, group_size=128
+- ## 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 DeepSeek-R1-Distill-Llama-8B
+    # single GPU
+    python quantize_quark.py --model_dir $MODEL_DIR \
+                            --output_dir output_dir $MODEL_NAME-awq-asym-uint4-g128-lmhead \
+                            --quant_scheme w_uint4_per_group_asym \
+                            --num_calib_data 128 \
+                            --quant_algo awq \
+                            --dataset pileval_for_awq_benchmark \
+                            --seq_len 512 \
+                            --model_export hf_format \
+                            --data_type bfloat16 \
+                            --exclude_layers
+    # cpu
+    python quantize_quark.py --model_dir $MODEL_DIR \
+                            --output_dir output_dir $MODEL_NAME-awq-asym-uint4-g128-lmhead \
+                            --quant_scheme w_uint4_per_group_asym \
+                            --num_calib_data 128 \
+                            --quant_algo awq \
+                            --dataset pileval_for_awq_benchmark \
+                            --seq_len 512 \
+                            --model_export hf_format \
+                            --data_type bfloat16 \
+                            --exclude_layers \
+                            --device cpu
+    ```
+## Deployment
+Quark has its own export format, quark_safetensors, which is compatible with autoAWQ exports.
+## 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>deepseek-ai/DeepSeek-R1-Distill-Llama-8B</strong>
+   </td>
+   <td><strong>amd/DeepSeek-R1-Distill-Llama-8B-awq-asym-uint4-g128-lmhead-onnx-cpu (this model)</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>Perplexity-wikitext2
+   </td>
+   <td>13.1432
+   </td>
+   <td>
+   </td>
+  </tr>
+  
+</table>
+
+#### License
+Modifications copyright(c) 2024 Advanced Micro Devices,Inc. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.