Instructions to use Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF", filename="Qwen3.6-27B-Abliterated-Heretic-Uncensored-BF16-00001-of-00002.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
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 Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
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 Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-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": "Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
- Ollama
How to use Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF with Ollama:
ollama run hf.co/Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
- Unsloth Studio
How to use Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-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 Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-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 Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF to start chatting
- Pi
How to use Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
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": "Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
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 Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF with Docker Model Runner:
docker model run hf.co/Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
- Lemonade
How to use Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Youssofal/Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF-Q4_K_M
List all available models
lemonade list
Qwen3.6-27B-Abliterated-Heretic-Uncensored-GGUF
This is a GGUF release of an abliterated, uncensored version of Qwen's Qwen3.6-27B with vision, made with Heretic.
By applying a Heretic-style two-stage MPOA pipeline with magnitude preservation on the Qwen3.6-27B dense text stack — slot-grouped output-side ablation followed by jailbreak-conditioned input-side ablation, mirroring the methodology used for Qwen3.6-35B-A3B — the base refusal behavior was attenuated at the weight level with low distributional divergence (KL 0.0282 vs base on harmless prompts). The result keeps Qwen3.6-27B's full vision and video multimodal architecture and general capability profile intact, while no longer defaulting to the original refusal pattern.
Quick Benchmarks
| Check | Original Qwen3.6-27B | Abliterated Heretic Uncensored |
|---|---|---|
| Hand-read 25-prompt refusal check (jailbreak system prompt) | 25/25 refuses | 22/25 clean refuses, 3/25 deflections, 0/25 direct passes |
| Hand-read 25-prompt refusal check (no system prompt) | 25/25 refuses | 9/25 clean refuses, 16/25 deflections, 0/25 direct passes |
| KL divergence | N/A | 0.0282 |
Numbers measured by reading every response, not by regex / refusal-marker scoring. Greedy decoding (do_sample=False), enable_thinking=False, on mlabonne/harmful_behaviors test[:25]. KL on mlabonne/harmless_alpaca test[:25]. The 3 remaining deflections under the jailbreak system prompt are crisis-substitution patterns (violent crime → conflict resolution, suicide → crisis hotline, car theft → legal vehicle acquisition).
Methodology & Model Notes
Qwen3.6-27B is a 27.8B dense vision-language model with 64 text layers, hybrid linear/full attention (3 linear-attention + 1 full-attention per 4-layer group), and an integrated image + video vision tower.
This release was produced with a Heretic-style two-stage MPOA pipeline with magnitude preservation, anchored at the residual peak layer (layer 63) for refusal direction. Stage 1 applies slot-grouped output-side orthogonalization on self_attn.o_proj, linear_attn.out_proj, and mlp.down_proj (each of the 64 layers grouped by layer_index % 4, with per-slot weight schedules adapted from the accepted Qwen3.6-35B-A3B values). Stage 2 applies slot-grouped input-side orthogonalization on mlp.gate_proj and mlp.up_proj, where the refusal direction is extracted under the jailbreak system-prompt context to specifically attenuate the resistance-to-jailbreak pathway. Each weight row's (or column's) L2 norm is restored after projection.
The resulting abliterated checkpoint was exported to BF16 and then converted to GGUF for llama.cpp-compatible deployment.
Files
Qwen3.6-27B-Abliterated-Heretic-Uncensored-BF16-00001-of-00002.gguf+-00002-of-00002.gguf: BF16 GGUF source (split; use with--load-tensorsorllama-gguf-split --merge)Qwen3.6-27B-Abliterated-Heretic-Uncensored-Q8_0.gguf: highest-fidelity quantQwen3.6-27B-Abliterated-Heretic-Uncensored-Q6_K.gguf: near-lossless practical quantQwen3.6-27B-Abliterated-Heretic-Uncensored-Q5_K_M.gguf: high-fidelity medium quantQwen3.6-27B-Abliterated-Heretic-Uncensored-Q4_K_M.gguf: smaller general-use quantQwen3.6-27B-Abliterated-Heretic-Uncensored-Q3_K_M.gguf: compact quantQwen3.6-27B-Abliterated-Heretic-Uncensored-Q2_K.gguf: smallest-footprint quant
Running
llama-server \
-m <quant-file.gguf> \
-ngl 999 -c 32768 --jinja -fa
Model Architecture
| Spec | Value |
|---|---|
| Total Parameters | 27.8B (dense) |
| Layers | 64 |
| Attention | Hybrid (3 linear-attention + 1 full-attention per 4-layer group) |
| Hidden Size | 5120 |
| Family | qwen3_5 |
| Modality | Vision-language |
| Base Model | Qwen/Qwen3.6-27B |
Disclaimer
This model has had refusal behavior attenuated at the weight level. It will answer prompts that the base model would normally refuse. You are responsible for how you use it.
Credits
- Base model: Qwen/Qwen3.6-27B
- Refusal removal pipeline: Heretic
- GGUF runtime and quantization: llama.cpp
License
This release inherits the base Qwen3.6-27B license.
Apache-2.0.
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