Image-Text-to-Text
Transformers
Safetensors
minimax_m3_vl
multimodal
Mixture of Experts
agent
coding
video
conversational
custom_code
Eval Results
8-bit precision
mxfp8
Instructions to use MiniMaxAI/MiniMax-M3.1-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiniMaxAI/MiniMax-M3.1-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="MiniMaxAI/MiniMax-M3.1-Flash", 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("MiniMaxAI/MiniMax-M3.1-Flash", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("MiniMaxAI/MiniMax-M3.1-Flash", trust_remote_code=True, device_map="auto") 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=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-M3.1-Flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MiniMaxAI/MiniMax-M3.1-Flash" # 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-M3.1-Flash", "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-M3.1-Flash
- SGLang
How to use MiniMaxAI/MiniMax-M3.1-Flash 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-M3.1-Flash" \ --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-M3.1-Flash", "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-M3.1-Flash" \ --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-M3.1-Flash", "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-M3.1-Flash with Docker Model Runner:
docker model run hf.co/MiniMaxAI/MiniMax-M3.1-Flash
Commit History
3 days ago
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Improve factual reliability and Jinja2 compatibility in chat template
Verified signature## Summary This PR proposes improvements to the MiniMax-M3.1-Flash chat template, primarily focusing on factual reliability, hallucination reduction, and standard Jinja2 compatibility. MiniMax-M3.1-Flash demonstrates strong overall capabilities, but its AA-Omniscience v1.1 performance appears relatively weak compared with its results on other benchmarks. Since AA-Omniscience penalizes incorrect answers, improving confidence calibration and reducing unsupported factual claims may help address this weakness. The proposed changes are intentionally minimal and do not modify the model's special tokens, message structure, reasoning protocol, or tool-calling format. ## Changes ### 1. Improve factual reliability and confidence calibration Added general-purpose instructions to the system prompt to encourage the model to: - Distinguish known facts from unsupported assumptions. - Avoid plausible-sounding guesses when factual information is insufficient. - Exercise greater caution with exa…c8c5b26
14 days ago
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Init MiniMaxAI/MiniMax-M3.1-Flash
Co-authored-by: ryanlee-dev <ryanlee-dev@users.noreply.huggingface.co> Co-authored-by: Xuebi <Xuebi@users.noreply.huggingface.co> Co-authored-by: rogeryoungh <rogeryoungh@users.noreply.huggingface.co>54e4e69