Instructions to use huihui-ai/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 huihui-ai/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="huihui-ai/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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated")
model = AutoModelForMultimodalLM.from_pretrained("huihui-ai/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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use huihui-ai/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 "huihui-ai/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": "huihui-ai/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/huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated

SGLang

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

Unsloth Studio

How to use huihui-ai/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 huihui-ai/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 huihui-ai/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 huihui-ai/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="huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated",
    max_seq_length=2048,
)

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

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

File size: 2,707 Bytes

c54befa

---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
base_model:
- lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled
tags:
- text-generation
- reasoning
- distillation
- chain-of-thought
- qwen
- qwen3.6
- mixture-of-experts
- moe
- lora
- unsloth
- abliterated
- uncensored
---

# 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](https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/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](https://github.com/ollama/ollama/releases)

You can use [huihui_ai/qwen3.6-abliterated:35b-Claude-4.7](https://ollama.com/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.


### Donation
##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
- bitcoin:
```
  bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
```
- Support our work on [Ko-fi](https://ko-fi.com/huihuiai)!