Update README.md

README.md

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----
-license: mit
----
+---
+base_model:
+- QuixiAI/MiniMax-M2.1-bf16
+language:
+- en
+library_name: transformers
+license: mit
+---
+
+# Model Overview
+
+- **Model Architecture:** Qwen3MoeForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Supported Hardware Microarchitecture:** AMD MI300 MI350/MI355
+- **ROCm**: 7.0
+- **PyTorch**: 2.8.0
+- **Transformers**: 4.57.6
+- **Operating System(s):** Linux
+- **Inference Engine:** [SGLang](https://docs.sglang.ai/)/[vLLM](https://docs.vllm.ai/en/latest/)
+- **Model Optimizer:** [AMD-Quark](https://quark.docs.amd.com/latest/index.html) (v0.11)
+  - **Weight quantization:** OCP MXFP4, Static
+  - **Activation quantization:** OCP MXFP4, Dynamic
+
+
+# Model Quantization
+
+The model was quantized from [QuixiAI/MiniMax-M2.1-bf16](https://huggingface.co/QuixiAI/MiniMax-M2.1-bf16) 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/
+export exclude_layers="lm_head *block_sparse_moe.gate* *self_attn*"
+python3 quantize_quark.py --model_dir $MODEL_DIR \
+                          --quant_scheme mxfp4 \
+                          --num_calib_data 128 \
+                          --exclude_layers $exclude_layers \
+                          --skip_evaluation \
+                          --multi_gpu  \
+                          --trust_remote_code \
+                          --model_export hf_format \
+                          --output_dir $output_dir
+```
+For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.
+
+# Evaluation
+The model was evaluated on gsm8k benchmarks using the [vllm](https://github.com/vllm-project/vllm/tree/v0.13.0) framework.
+
+### Accuracy
+
+<table>
+  <tr>
+   <td><strong>Benchmark</strong>
+   </td>
+   <td><strong>QuixiAI/MiniMax-M2.1-bf16 </strong>
+   </td>
+   <td><strong>amd/MiniMax-M2.1-MXFP4(this model)</strong>
+   </td>
+   <td><strong>Recovery</strong>
+   </td>
+  </tr>
+  <tr>
+   <td>gsm8k 
+   </td>
+   <td>0.9356
+   </td>
+   <td>0.9348
+   </td>
+   <td>99.91%
+   </td>
+  </tr>
+</table>
+
+
+### Reproduction
+
+The GSM8K results were obtained using the vLLM framework, based on the Docker image `rocm/vllm:rocm7.0.0_vllm_0.11.2_20251210`, and vLLM is installed from source inside the container.
+
+#### Preparation in container
+```
+# Reinstall vLLM
+pip uninstall vllm -y
+git clone https://github.com/vllm-project/vllm.git
+cd vllm
+git checkout v0.13.0
+pip install -r requirements/rocm.txt
+python setup.py develop
+cd ..
+```
+
+#### Launching server
+```
+VLLM_ROCM_USE_AITER=1 \
+VLLM_DISABLE_COMPILE_CACHE=1 \
+vllm serve "$MODEL" \
+    --tensor-parallel-size 4 \
+    --trust-remote-code \
+    --max-model-len 32768 \
+    --port 8899  
+```
+
+
+#### Evaluating model in a new terminal
+```
+python vllm/tests/evals/gsm8k/gsm8k_eval.py --host http://127.0.0.1 --port 8899 --num-questions 1000 --save-results logs
+```
+
+
+# License
+Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.