Instructions to use amd/MiniMax-M3-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amd/MiniMax-M3-MXFP4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="amd/MiniMax-M3-MXFP4", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("amd/MiniMax-M3-MXFP4", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("amd/MiniMax-M3-MXFP4", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amd/MiniMax-M3-MXFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amd/MiniMax-M3-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-M3-MXFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/amd/MiniMax-M3-MXFP4

SGLang

How to use amd/MiniMax-M3-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-M3-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-M3-MXFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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-M3-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-M3-MXFP4",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use amd/MiniMax-M3-MXFP4 with Docker Model Runner:
```
docker model run hf.co/amd/MiniMax-M3-MXFP4
```

MiniMax-M3-MXFP4 / README.md

jiaxwang

Update README.md

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	---
	pipeline_tag: image-text-to-text
	license: other
	license_name: minimax-community
	license_link: LICENSE
	library_name: transformers
	tags:
	- multimodal
	- moe
	- agent
	- coding
	- video
	---

	# Model Overview

	- Model Architecture: MiniMaxM3SparseForConditionalGeneration
	- Input: Text, Image
	- Output: Text
	- Supported Hardware Microarchitecture: AMD MI350/MI355
	- ROCm: 7.1.1
	- 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)
	- Weight quantization: OCP MXFP4, Static
	- Activation quantization: OCP MXFP4, Dynamic


	# Model Quantization

	The model was quantized from [MiniMaxAI/MiniMax-M3](https://huggingface.co/MiniMaxAI/MiniMax-M3) 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/
	exclude_layers="lm_head vision_tower* multi_modal_projector patch_merge_mlp block_sparse_moe.gate self_attn* mlp.gate_proj mlp.up_proj *mlp.down_proj"
	CUDA_VISIBLE_DEVICES=0 python3 quantize_quark.py \
	--model_dir MiniMaxAI/MiniMax-M3 \
	--quant_scheme mxfp4 \
	--exclude_layers $exclude_layers \
	--output_dir /mnt/amd/MiniMax-M3-MXFP4 \
	--file2file_quantization
	```

	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

	<table>
	<tr>
	<td><strong>Benchmark</strong>
	</td>
	<td><strong>MiniMaxAI/MiniMax-M3 </strong>
	</td>
	<td><strong>amd/MiniMax-M3-MXFP4(this model)</strong>
	</td>
	<td><strong>Recovery</strong>
	</td>
	</tr>
	<tr>
	<td>gsm8k (flexible-extract)
	</td>
	<td>95.30
	</td>
	<td>94.19
	</td>
	<td>98.84%
	</td>
	</tr>
	</table>


	### Reproduction

	The GSM8K results were obtained using the lm-eval framework, based on the
	Docker image `rocm/pytorch-private:vllm-hy-mm-06112026`. The vLLM shipped in
	that image was used as-is, with the patch from this PR ([#45794](https://github.com/vllm-project/vllm/pull/45794/changes)) applied on top.

	#### Launching server
	```
	vllm serve /mnt/amd/MiniMax-M3-MXFP4 \
	--trust-remote-code \
	--block-size 128 \
	--tensor-parallel-size 8 \
	--attention-backend TRITON_ATTN \
	--mm-encoder-tp-mode data \
	--mm-encoder-attn-backend ROCM_AITER_FA \
	--tool-call-parser minimax_m3 \
	--enable-auto-tool-choice \
	--reasoning-parser minimax_m3 \
	--moe-backend emulation
	```


	#### Evaluating model in a new terminal
	```
	lm_eval \
	--model local-chat-completions \
	--model_args "model=/mnt/amd/MiniMax-M3-MXFP4,base_url=http://127.0.0.1:8000/v1/chat/completions,num_concurrent=32,max_gen_toks=16384" \
	--tasks gsm8k \
	--num_fewshot 5 \
	--batch_size 1 \
	--apply_chat_template \
	--fewshot_as_multiturn
	```