Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated

Instructions to use ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated")
model = AutoModelForImageTextToText.from_pretrained("ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-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?"}
        ]
    },
]
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

vLLM

How to use ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-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": "ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated

SGLang

How to use ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-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 "ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-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": "ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-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": "ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated",
    max_seq_length=2048,
)

Docker Model Runner
How to use ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated with Docker Model Runner:
```
docker model run hf.co/ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated
```

huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated

This is an uncensored version of lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.

ollama

Please use the latest version of ollama

You can use huihui_ai/qwen3.6-abliterated:35b-Claude-4.7 directly,

ollama run huihui_ai/Qwen3.6-abliterated:35b-Claude-4.7

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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Safetensors

Model size

36B params

Tensor type

BF16

Model tree for ansulev/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated

Base model

Qwen/Qwen3.6-35B-A3B

Adapter

lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled

Adapter

(14)

this model