Model Overview

Model Architecture: MiniMaxM2ForCausalLM
- Input: Text
- Output: Text
Supported Hardware Microarchitecture: AMD MI300/MI350/MI355 (emulation)
ROCm: 7.2.2
PyTorch: 2.10.0
Transformers: 5.2.0
Operating System(s): Linux
Inference Engine: SGLang/vLLM
Model Optimizer: AMD-Quark (v0.12)
- Quantized layers: experts
- Weight quantization: NVFP4, Static
- Activation quantization: NVFP4, Dynamic

Model Quantization

The model was quantized from MiniMaxAI/MiniMax-M2.5 by using AMD-Quark. The weights and activations are quantized to NVFP4.

Quantization scripts:

cd Quark/examples/torch/language_modeling/llm_ptq/
export exclude_layers="lm_head *block_sparse_moe.gate* *self_attn*"
export CUDA_VISIBLE_DEVICES=0,1,2,3
python3 quantize_quark.py \
  --model_dir MiniMaxAI/MiniMax-M2.5 \
  --quant_scheme nvfp4 \
  --num_calib_data 128 \
  --exclude_layers $exclude_layers \
  --model_export hf_format  \
  --trust_remote_code \
  --multi_gpu \
  --output_dir amd/MiniMax-M2.5-NVFP4

For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.

Deployment

Use with vLLM/SGLang

This model can be deployed efficiently using the vLLM and SGLang backends.

Evaluation

The model was evaluated on gsm8k benchmarks using the vllm framework.

Accuracy

Benchmark	MiniMaxAI/MiniMax-M2.5	amd/MiniMax-M2.5-NVFP4(this model)	Recovery
gsm8k (flexible-extract)	91.51	91.21	99.67%

Reproduction

The GSM8K result was obtained using the lm-evaluation-harness framework, based on the Docker image rocm/vllm-dev:nightly_main_20260603.

Install the lm-eval (Version: 0.4.12) in container first.

pip install lm-eval[api]

Launching server

VLLM_ROCM_USE_AITER=1 vllm serve amd/MiniMax-M2.5-NVFP4/ \
  --tensor-parallel-size 2 \
  --tool-call-parser minimax_m2 \
  --reasoning-parser minimax_m2 \
  --enable-auto-tool-choice \
  --trust-remote-code

Evaluating model in a new terminal

lm_eval \
  --model local-completions \
  --model_args "model=amd/MiniMax-M2.5-NVFP4/,base_url=http://127.0.0.1:8000/v1/completions,tokenized_requests=False,tokenizer_backend=None,num_concurrent=32" \
  --gen_kwargs temperature=1.0,top_p=0.95 \
  --tasks gsm8k \
  --num_fewshot 8 \
  --batch_size 1

MiniMax-M2.5-NVFP4

Model card