Instructions to use OpenYourMind/Minimax-M3-abliterated-clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenYourMind/Minimax-M3-abliterated-clean with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="OpenYourMind/Minimax-M3-abliterated-clean", 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("OpenYourMind/Minimax-M3-abliterated-clean", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("OpenYourMind/Minimax-M3-abliterated-clean", 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=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OpenYourMind/Minimax-M3-abliterated-clean with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenYourMind/Minimax-M3-abliterated-clean" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenYourMind/Minimax-M3-abliterated-clean", "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/OpenYourMind/Minimax-M3-abliterated-clean
- SGLang
How to use OpenYourMind/Minimax-M3-abliterated-clean 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 "OpenYourMind/Minimax-M3-abliterated-clean" \ --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": "OpenYourMind/Minimax-M3-abliterated-clean", "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 "OpenYourMind/Minimax-M3-abliterated-clean" \ --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": "OpenYourMind/Minimax-M3-abliterated-clean", "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 OpenYourMind/Minimax-M3-abliterated-clean with Docker Model Runner:
docker model run hf.co/OpenYourMind/Minimax-M3-abliterated-clean
Thanks
Thank you for your work and for not gatekeeping this like... others.
Is it possible to include the refusal rates of this?
well, i am not too much of a fan of Refusal rate reporting (as everyone else does) just because the refusal rate differs as everyone uses different dattasets and they just mean "nothing". We use our Severe Harmfull prompt set for refusal detection and a clean ablation is always 0/1300 after ablation with 0 garble on 500 non harmfull counter tested.
That people try to make money of a opensource tools is just hillarious imho. If you like it you can sponsor me, if not you can also have fun and enjoy it. I do ablations because i love the math and the riddle (thats why i build my own framework), not because i want to make money (i do have a job). Even though ablations are an expensive hobby i appreciate any help.