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README.md

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-license: mit
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+---
+license: mit
+base_model:
+- deepseek-ai/DeepSeek-R1
+---
+
+
+# Model Overview
+
+- **Model Architecture:** DeepSeek-R1
+  - **Input:** Text
+  - **Output:** Text
+- **Supported Hardware Microarchitecture:** AMD MI350/MI355
+- **ROCm**: 7.0
+- **Operating System(s):** Linux
+- **Inference Engine:** [SGLang](https://docs.sglang.ai/)
+- **Model Optimizer:** [AMD-Quark](https://quark.docs.amd.com/latest/index.html)
+  - **Weight quantization:** OCP MXFP4, Static
+  - **Activation quantization:** OCP MXFP4, Dynamic
+- **Calibration Dataset:** [Pile](https://huggingface.co/datasets/mit-han-lab/pile-val-backup)
+
+This model was built with deepseek-ai DeepSeek-R1 model by applying [AMD-Quark](https://quark.docs.amd.com/latest/index.html) for MXFP4 quantization.
+
+# Model Quantization
+
+The model was quantized from [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). Both weights and activations were quantized to MXFP4 format, and the AutoSmoothQuant algorithm was applied to enhance accuracy. 
+
+**Preprocessing requirement:**
+
+Before executing the quantization script below, the original FP8 model must first be dequantized to BFloat16.
+You can either perform the dequantization manually using this [conversion script](https://github.com/deepseek-ai/DeepSeek-V3/blob/main/inference/fp8_cast_bf16.py), or use the pre-converted BFloat16 model available at [unsloth/DeepSeek-R1-BF16](https://huggingface.co/unsloth/DeepSeek-R1-BF16).
+
+**Quantization scripts:**
+```
+cd Quark/examples/torch/language_modeling/llm_ptq/
+exclude_layers="*self_attn* *mlp.gate.* *lm_head"
+python3 quantize_quark.py --model_dir $MODEL_DIR \
+                          --quant_scheme w_mxfp4_a_mxfp4 \
+                          --group_size 32 \
+                          --num_calib_data 128 \
+                          --exclude_layers $exclude_layers \
+                          --multi_gpu \
+                          --quant_algo autosmoothquant \
+                          --model_export hf_format \
+                          --output_dir amd/DeepSeek-R1-MXFP4-Preview-ASQ
+```
+
+# Deployment
+### Use with SGLang
+
+This model can be deployed efficiently using the [SGLang](https://docs.sglang.ai/) backend.
+
+# License
+Modifications Copyright(c) 2025 Advanced Micro Devices, Inc. All rights reserved.