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base_model:
- MiniMaxAI/MiniMax-M2.5
language:
- en
library_name: transformers
license: other
license_name: modified-mit
license_link: https://github.com/MiniMax-AI/MiniMax-M2.5/blob/main/LICENSE
---
# 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](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 [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5) which was converted to bf16 using [QuixiAI/MiniMax-M2.1-bf16/minimax_to_bf16.py](https://huggingface.co/QuixiAI/MiniMax-M2.1-bf16/blob/main/minimax_to_bf16.py) 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 $output_dir
```
For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.
# Evaluation
The model was evaluated on gsm8k benchmarks using the [vllm](https://github.com/vllm-project/vllm/tree/v0.13.0) framework.
### Accuracy
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>MiniMaxAI/MiniMax-M2.5 </strong>
</td>
<td><strong>amd/MiniMax-M2.5-MXFP4(this model)</strong>
</td>
<td><strong>Recovery</strong>
</td>
</tr>
<tr>
<td>gsm8k (flexible-extract)
</td>
<td>0.9401
</td>
<td>0.9256
</td>
<td>98.46%
</td>
</tr>
</table>
### Reproduction
The GSM8K results were obtained using the lm-eval framework, based on the Docker image `rocm/vllm-dev:nightly`.
#### Evaluating model in a new terminal
```
export VLLM_ROCM_USE_AITER=1
export model_dir=MiniMaxAI/MiniMax-M2.5-MXFP4/
log_file=minimax25-lm_eval_gsm8k_mxfp4.txt
lm_eval --model vllm --model_args pretrained=$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
```
# License
Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved. |