Instructions to use huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-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("huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4") model = AutoModelForMultimodalLM.from_pretrained("huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-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 huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-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": "huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-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/huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4
- SGLang
How to use huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-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 "huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-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": "huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-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 "huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-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": "huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-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 huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4 with Docker Model Runner:
docker model run hf.co/huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4
huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4
This is the NVFP4 quantitative version of huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated created using vllm-project/llm-compressor
Note
This is just an attempt at NVFP4 quantization; no further tests have been conducted. If there are any issues, please leave a message.
VLLM
1. Environmental installation
uv self update
cd /vllm/vllm-project/
mkdir venv
cd /vllm/vllm-project/venv
uv venv --python 3.12 --seed --managed-python
cd /vllm/vllm-project/venv
source .venv/bin/activate
cd /vllm/vllm-project
git clone ssh://git@github.com/vllm-project/vllm
cd /vllm/vllm-project/vllm
pip install -e .
2. Environment validation
uv pip show vllm
Version: 0.18.1rc1.dev115+gd6bb2a9d9.cu130
uv pip show compressed-tensors
Version: 0.14.0.1
uv pip show transformers
Version: 4.57.6
uv pip show torch
Version: 2.10.0+cu130
uv pip show llmcompressor
warning: Package(s) not found for: llmcompressor
3. Download the model.
cd /vllm/models
hf download huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4 --local-dir ./huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4 --token xxx
4. Load the model
cd /vllm/vllm-project/venv
source .venv/bin/activate
export CUDA_VISIBLE_DEVICES=0
unset LD_LIBRARY_PATH
export VLLM_USE_FLASHINFER_MOE_FP4=0
export VLLM_NVFP4_GEMM_BACKEND=cutlass
cd /vllm/models
vllm serve huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4 \
--tensor-parallel-size 1 \
--max-model-len 16384 \
--trust-remote-code \
--skip-mm-profiling \
--limit-mm-per-prompt '{"image": 0, "video": 0}' \
--enforce-eager \
--gpu-memory-utilization 0.78 \
--disable-custom-all-reduce
5. Simple test
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4",
"messages": [
{"role": "user", "content": "hello"}
],
"temperature": 0.7,
"top_p": 0.9,
"max_tokens": 8192,
"stream": false
}'
6. Issue
If you encounter the following issue: ValueError: The Tokenizer class TokenizersBackend does not exist or is not currently imported.
Modify tokenizer_config.json
TokenizersBackend-> Qwen2Tokenizer
"tokenizer_class": "TokenizersBackend",
Change it to
"tokenizer_class": "Qwen2Tokenizer",
Usage Warnings
Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
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Model tree for huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4
Base model
Qwen/Qwen3.5-35B-A3B-Base
docker model run hf.co/huihui-ai/Huihui-Qwen3.5-35B-A3B-abliterated-NVFP4