Instructions to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF") 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 AutoModel model = AutoModel.from_pretrained("huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
Use Docker
docker model run hf.co/huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF 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-abliterated-MTP-GGUF" # 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-abliterated-MTP-GGUF", "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-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
- SGLang
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF 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-abliterated-MTP-GGUF" \ --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-abliterated-MTP-GGUF", "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-Qwen3.6-35B-A3B-abliterated-MTP-GGUF" \ --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-abliterated-MTP-GGUF", "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" } } ] } ] }' - Ollama
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF with Ollama:
ollama run hf.co/huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
- Unsloth Studio
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF 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-abliterated-MTP-GGUF 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-abliterated-MTP-GGUF 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-abliterated-MTP-GGUF to start chatting
- Pi
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF with Docker Model Runner:
docker model run hf.co/huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
- Lemonade
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
Run and chat with the model
lemonade run user.Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF-Q2_K
List all available models
lemonade list
- Hermes Agent
How to use huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF:Q2_K
Run Hermes
hermes
- Atomic Chat
Create README.md
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---
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library_name: transformers
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license: apache-2.0
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license_link: https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/LICENSE
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pipeline_tag: image-text-to-text
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base_model:
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- Qwen/Qwen3.6-35B-A3B
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tags:
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- abliterated
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- uncensored
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- GGUF
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- MTP
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---
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# huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF
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This is an uncensored version of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
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This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
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## GGUF
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Please use the latest version of [ggml-org/llama.cpp](https://github.com/ggml-org/llama.cpp/releases)
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```
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apt-get update
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apt-get install pciutils build-essential cmake curl libcurl4-openssl-dev -y
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git clone https://github.com/ggml-org/llama.cpp
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cmake llama.cpp -B llama.cpp/build \
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-DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
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cmake --build llama.cpp/build --config Release -j --clean-first --target llama-cli llama-mtmd-cli llama-server llama-gguf-split
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cp llama.cpp/build/bin/llama-* llama.cpp
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```
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```
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./llama.cpp/llama-cli \
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-m huihui-ai/Huihui-Qwen3.6-35B-A3B-abliterated-MTP-GGUF/Huihui-Qwen3.6-35B-A3B-abliterated-ggml-model-Q4_K.gguf \
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-ngl 99 -c 262144 -fa on -np 1 \
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--spec-type draft-mtp --spec-draft-n-max 6
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```
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### Usage Warnings
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- **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.
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- **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.
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- **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.
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- **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.
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- **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.
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- **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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### Donation
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##### Your donation helps us continue our further development and improvement, a cup of coffee can do it.
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- bitcoin:
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```
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bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge
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```
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- Support our work on [Ko-fi](https://ko-fi.com/huihuiai)!
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