Instructions to use amd/MiniMax-M2.7-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/MiniMax-M2.7-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="amd/MiniMax-M2.7-NVFP4", 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-M2.7-NVFP4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("amd/MiniMax-M2.7-NVFP4", 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 Settings

vLLM

How to use amd/MiniMax-M2.7-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/MiniMax-M2.7-NVFP4"
# 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-M2.7-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/amd/MiniMax-M2.7-NVFP4

SGLang

How to use amd/MiniMax-M2.7-NVFP4 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-M2.7-NVFP4" \
    --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-M2.7-NVFP4",
		"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-M2.7-NVFP4" \
        --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-M2.7-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use amd/MiniMax-M2.7-NVFP4 with Docker Model Runner:
```
docker model run hf.co/amd/MiniMax-M2.7-NVFP4
```

MiniMax-M2.7-NVFP4 / README.md

linzhao-amd

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	---
	base_model:
	- MiniMaxAI/MiniMax-M2.7
	language:
	- en
	library_name: transformers
	license: other
	license_name: modified-mit
	license_link: https://huggingface.co/MiniMaxAI/MiniMax-M2.7/blob/main/LICENSE
	---
	# 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: [vLLM](https://docs.vllm.ai/en/latest/)
	- Model Optimizer: [AMD-Quark](https://quark.docs.amd.com/latest/index.html) (v0.12)
	- 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)
	- Quantized layers: `experts`
	- Weight quantization: NVFP4, Static
	- Activation quantization: NVFP4, Dynamic


	# Model Quantization

	The model was quantized from [amd/MiniMax-M2.7-BF16](https://huggingface.co/amd/MiniMax-M2.7-BF16), originally from [MiniMax/MiniMax-M2.7](https://huggingface.co/MiniMax/MiniMax-M2.7), using [AMD-Quark](https://quark.docs.amd.com/latest/index.html). The weights and activations are quantized to NVFP4.


	Quantization scripts:
	```
	cd Quark/examples/torch/language_modeling/llm_ptq
	exclude_layers="lm_head block_sparse_moe.gate self_attn"
	python3 quantize_quark.py --model_dir amd/MiniMax-M2.7-BF16 \
	--quant_scheme nvfp4 \
	--exclude_layers $exclude_layers \
	--num_calib_data 128 \
	--model_export hf_format \
	--multi_gpu balanced \
	--trust_remote_code \
	--output_dir amd/MiniMax-M2.7-NVFP4
	```


	For further details or issues, please refer to the AMD-Quark documentation or contact the respective developers.
	# Deployment
	## Evaluation
	The model was evaluated on gsm8k benchmarks using the vllm framework.

	### Accuracy

	<table>
	<tr>
	<td><strong>Benchmark</strong>
	</td>
	<td><strong>MiniMaxAI/MiniMax-M2.7 </strong>
	</td>
	<td><strong>amd/MiniMax-M2.7-NVFP4(this model)</strong>
	</td>
	<td><strong>Recovery</strong>
	</td>
	</tr>
	<tr>
	<td>gsm8k (flexible-extract)
	</td>
	<td>91.81
	</td>
	<td>92.20
	</td>
	<td>100.04%
	</td>
	</tr>
	</table>


	### 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
	pip install lm-eval[api]
	```
	#### Launching server
	```
	vllm serve \
	--model amd/MiniMax-M2.7-NVFP4 \
	--trust-remote-code \
	--host 0.0.0.0 \
	--port 8011 \
	--tensor-parallel-size 4 \
	--enable-auto-tool-choice \
	--tool-call-parser minimax_m2 \
	--reasoning-parser minimax_m2_append_think
	```


	#### Evaluating model in a new terminal
	```
	python3 vllm/tests/evals/gsm8k/gsm8k_eval.py --host http://0.0.0.0 --port 8011
	```


	# License
	Modifications Copyright(c) 2026 Advanced Micro Devices, Inc. All rights reserved.