Update README.md

README.md

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-**Disclaimer**
+---
+pipeline_tag: image-text-to-text
+license: other
+license_name: minimax-community
+license_link: LICENSE
+library_name: transformers
+tags:
+  - multimodal
+  - moe
+  - agent
+  - coding
+  - video
+---
 
-This model is provided for research and evaluation purposes only.
-Quantization may introduce accuracy or behavioral differences compared to the original model.
-Users are responsible for validating the model in their own environments and complying with the original model license.
+# Model Overview
+
+- **Model Architecture:** MiniMaxM3SparseForConditionalGeneration
+  - **Input:** Text, Image
+  - **Output:** Text
+- **Supported Hardware Microarchitecture:** AMD MI350/MI355
+- **ROCm**: 7.1.1
+- **PyTorch**: 2.10.0
+- **Transformers**: 5.2.0
+- **Operating System(s):** Linux
+- **Inference Engine:** [vLLM](https://docs.vllm.ai/en/latest/)
+- **Model Optimizer:** [AMD-Quark](https://quark.docs.amd.com/latest/index.html)
+  - **Weight quantization:** OCP MXFP4, Static
+  - **Activation quantization:** OCP MXFP4, Dynamic
+
+
+# Model Quantization
+
+The model was quantized from [MiniMaxAI/MiniMax-M3](https://huggingface.co/MiniMaxAI/MiniMax-M3) using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). The weights are quantized to MXFP4 and activations are quantized to MXFP4.
+
+
+**Quantization scripts:**
+```
+cd Quark/examples/torch/language_modeling/llm_ptq/
+exclude_layers="*lm_head *vision_tower* *multi_modal_projector* *patch_merge_mlp* *block_sparse_moe.gate *self_attn* *mlp.gate_proj *mlp.up_proj *mlp.down_proj"
+CUDA_VISIBLE_DEVICES=0 python3 quantize_quark.py \
+  --model_dir MiniMaxAI/MiniMax-M3 \
+  --quant_scheme mxfp4 \
+  --exclude_layers $exclude_layers \
+  --output_dir /mnt/amd/MiniMax-M3-MXFP4 \
+  --file2file_quantization
+```
+
+For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.
+
+# Evaluation
+The model was evaluated on gsm8k benchmarks using the vllm framework.
+
+### Accuracy
+
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>MiniMaxAI/MiniMax-M3 </strong>
+   </td>
+   <td><strong>amd/MiniMax-M3-MXFP4(this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>gsm8k (flexible-extract) 
+   </td>
+   <td>95.30
+   </td>
+   <td>94.19
+   </td>
+   <td>98.84%
+   </td>
+  </tr>
+</table>
+
+
+### Reproduction
+
+The GSM8K results were obtained using the lm-eval framework, based on the
+Docker image `rocm/pytorch-private:vllm-hy-mm-06112026`. The vLLM shipped in
+that image was used as-is, with the patch from this PR ([#45794](https://github.com/vllm-project/vllm/pull/45794/changes)) applied on top.
+
+#### Launching server
+```
+vllm serve /mnt/amd/MiniMax-M3-MXFP4 \
+  --trust-remote-code \
+  --block-size 128 \
+  --tensor-parallel-size 8 \
+  --attention-backend TRITON_ATTN \
+  --mm-encoder-tp-mode data \
+  --mm-encoder-attn-backend ROCM_AITER_FA \
+  --tool-call-parser minimax_m3 \
+  --enable-auto-tool-choice \
+  --reasoning-parser minimax_m3 \
+  --moe-backend emulation
+```
+
+
+#### Evaluating model in a new terminal
+```
+lm_eval \
+  --model local-chat-completions \
+  --model_args "model=/mnt/amd/MiniMax-M3-MXFP4,base_url=http://127.0.0.1:8000/v1/chat/completions,num_concurrent=32,max_gen_toks=16384" \
+  --tasks gsm8k \
+  --num_fewshot 5 \
+  --batch_size 1 \
+  --apply_chat_template \
+  --fewshot_as_multiturn
+```