Instructions to use llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4") 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("llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4") model = AutoModelForMultimodalLM.from_pretrained("llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4", 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 llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4", "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/llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4
- SGLang
How to use llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4 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 "llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4" \ --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": "llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4", "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 "llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4" \ --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": "llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4", "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 llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4 with Docker Model Runner:
docker model run hf.co/llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4
YESSS !!! YOUR THE MAN
β€οΈβπ₯β€οΈβπ₯β€οΈβπ₯
β€οΈβπ₯β€οΈβπ₯β€οΈβπ₯
I'll be making some GGUFs in a few minutes, I'll have them uploaded very soon, after that I'll try making some GPTQ-Int4 too.
Credit where it's due... it's still a strong model. MTP only sweetens the deal, heh
Here you go, wasn't easy making these due to lots of roadblocks and compatibility issues, but was able to do it in the end, it should be super fast if you have blackwell + MTP support:
https://huggingface.co/llmfan46/Qwen3.5-27B-uncensored-heretic-v2-Native-MTP-Preserved-NVFP4-GGUF