Instructions to use huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated") 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("huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated") model = AutoModelForMultimodalLM.from_pretrained("huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated", 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 huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated", "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/huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated
- SGLang
How to use huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated 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 "huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated" \ --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": "huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated", "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 "huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated" \ --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": "huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated", "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 huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated with Docker Model Runner:
docker model run hf.co/huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated
Is there a plan for an FP8 version or a GGUF version that can be used in ComfyUI?
Thank you to the author for the open-source spirit, respect. May I ask the author: Is there a plan for an FP8 version or a GGUF version that can be used in ComfyUI?
I asked AIs this and get different answers... in your experience, is f32 of 4b better than q8_0 of 8b? similarly, q4_K_M of the 32b vs 8b in f32? it is maybe stupid to ask, but I can't get a straight answer out of anyone...
If long context is the focus, choose the large model's Q4; if speed is prioritized, choose the small model's f32.
thanks!! that is actually helpful
Is there a plan for an FP8 version
fp8 version ?
https://github.com/huggingface/transformers/blob/main/docs/source/en/quantization/fbgemm_fp8.md
Or perhaps we fine-tune an FP8 version later.
How to use this gguf model in ComfyUI? Which node should be used for loading it?