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 MiniMaxAI/MiniMax-M2.5 which was converted to bf16 using QuixiAI/MiniMax-M2.1-bf16/minimax_to_bf16.py 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

Benchmark	MiniMaxAI/MiniMax-M2.5	amd/MiniMax-M2.5-MXFP4(this model)	Recovery
gsm8k (flexible-extract)	0.9401	0.9256	98.46%

Reproduction

The GSM8K results were obtained using the lm-eval framework, based on the Docker image rocm/vllm-dev:nightly.

Evaluating model in a new terminal

export VLLM_ROCM_USE_AITER=1

export model_dir=MiniMaxAI/MiniMax-M2.5-mxfp4/
log_file=minimax25-lm_eval_gsm8k_mxfp4.txt

lm_eval --model vllm --model_args pretrained=$model_dir,enforce_eager=True,trust_remote_code=True,max_model_len=16384 \
        --gen_kwargs temperature=1.0,top_p=0.95,top_k=40 \
        --tasks gsm8k  --num_fewshot 8 2>&1 | tee $log_file