Instructions to use amd/MiniMax-M2.5-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/MiniMax-M2.5-MXFP4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="amd/MiniMax-M2.5-MXFP4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("amd/MiniMax-M2.5-MXFP4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("amd/MiniMax-M2.5-MXFP4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use amd/MiniMax-M2.5-MXFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/MiniMax-M2.5-MXFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/MiniMax-M2.5-MXFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amd/MiniMax-M2.5-MXFP4

SGLang

How to use amd/MiniMax-M2.5-MXFP4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "amd/MiniMax-M2.5-MXFP4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/MiniMax-M2.5-MXFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "amd/MiniMax-M2.5-MXFP4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amd/MiniMax-M2.5-MXFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amd/MiniMax-M2.5-MXFP4 with Docker Model Runner:
```
docker model run hf.co/amd/MiniMax-M2.5-MXFP4
```

MiniMax-M2.5-MXFP4 / README.md

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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.