Model Overview
- Model Architecture: MiniMaxM2ForCausalLM
- Input: Text
- Output: Text
- Supported Hardware Microarchitecture: AMD MI300 MI350/MI355
- ROCm: 7.0
- PyTorch: 2.8.0
- Transformers: 4.57.1
- Operating System(s): Linux
- Inference Engine: SGLang/vLLM
- Model Optimizer: AMD-Quark (v0.11)
- Weight quantization: OCP MXFP4, Static
- Activation quantization: OCP MXFP4, Dynamic
Model Quantization
The model was quantized from QuixiAI/MiniMax-M2.1-bf16 using AMD-Quark. 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 $output_dir
For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.
Evaluation
The model was evaluated on gsm8k benchmarks using the vllm framework.
Accuracy
Reproduction
The GSM8K results were obtained using the vLLM framework, based on the Docker image rocm/vllm-dev:nightly_main_20260211, and vLLM is installed inside the container.
Preparation in container
To download the evaluation script, reinstallation is not required.
# Install vLLM code repo
git clone https://github.com/vllm-project/vllm.git
cd vllm
git checkout v0.13.0
cd ..
Launching server
VLLM_ROCM_USE_AITER=1 \
VLLM_DISABLE_COMPILE_CACHE=1 \
vllm serve "$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.