Create README.md

README.md

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+---
+base_model: databricks/dbrx-instruct
+---
+# dbrx_moe_fp8_test
+- ## 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" and "router.layer"
+  - ***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 databricks/dbrx-instruct
+# single GPU
+python3 quantize_quark.py \ 
+        --model_dir $MODEL_DIR \
+        --output_dir dbrx_moe_fp8_test \                           
+        --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 dbrx_moe_fp8_test\
+        --quant_scheme w_fp8_a_fp8 \
+        --kv_cache_dtype fp8 \
+        --num_calib_data 128 \
+        --multi_gpu \
+        --model_export quark_safetensors \
+        --no_weight_matrix_merge
+```
+## Deployment
+Quark has its own export format and allows FP8 quantized models to be efficiently deployed using the vLLM backend(vLLM-compatible).
+In the dbrx-instruct model, "transformer.blocks.\*.ffn.experts" modules can be divided into experts-num mlps, and if the shape of the weight of w1 in one of the mlps is [dim1, dim2], 
+then the shape of “transformer.blocks.\*.ffn.experts.mlp.w1.weight“ in the exported safetensors file is [dim1\*experts-num, dim2]. The shapes of "transformer.blocks.\*.ffn.experts.mlp.w1.weight_scale" 
+and "transformer.blocks.\*.ffn.experts.mlp.w1.input_scale" are [dim1]. Similarly, this also applies to the w2 and v1 of "transformer.blocks.\*.ffn.experts.mlp".
+## 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>dbrx-instruct </strong>
+   </td>
+   <td><strong>dbrx_moe_fp8_test(this model)</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>Perplexity-wikitext2
+   </td>
+   <td>4.2275
+   </td>
+   <td>4.3033
+   </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.