Instructions to use Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF", filename="Qwen-3.5-27B-Opus-Reasoning-Abliterated-EP3.5_bpw.gguf", )
llm.create_chat_completion( 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" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K # Run inference directly in the terminal: llama-cli -hf Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-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 Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-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 Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K
Use Docker
docker model run hf.co/Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K
- LM Studio
- Jan
- vLLM
How to use Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-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": "Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-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/Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K
- Ollama
How to use Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF with Ollama:
ollama run hf.co/Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K
- Unsloth Studio
How to use Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-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 Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-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 Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF to start chatting
- Pi
How to use Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-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": "Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-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 Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-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 Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF with Docker Model Runner:
docker model run hf.co/Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K
- Lemonade
How to use Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF:Q2_K
Run and chat with the model
lemonade run user.Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF-Q2_K
List all available models
lemonade list
Note: This release currently does not have vision capabilities due to an oversight.
I'll get this fixed as soon as my free lightning ai credits reset (or please get in touch if you would like to sponsor some A100 hours).
Qwen 3.5 27B Opus-Reasoning v2 (Abliterated) - Mixed Precision GGUFs
This repository features traditional and EvoPress GGUF quants of an abliterated reasoning model. Built upon Jackrong's Claude-4.6-Opus Distillation v2, the model was uncensored via the Orion-Zhen pipeline before being quantized with the EvoPress mixed-precision strategy.
🔥 Model Lineage & Highlights
- Base: Jackrong/Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled-v2
- Distilled using 14,000+ Claude 4.6 Opus-style samples to drastically improve Chain-of-Thought (CoT) efficiency.
- Reduces unnecessarily long internal reasoning chains while maintaining top-tier benchmark scores (e.g., 96.91% pass@1 on HumanEval).
- Abliteration: Processed via Orion-Zhen's open-source pipeline.
⚡ EvoPress Mixed-Precision Quantization
This release utilizes the EvoPress methodology to maximize intelligence-per-gigabyte in Hybrid Mamba architectures. Standard quantization often degrades the sensitive State Space Model (SSM) components; these quants solve that by using tiered-precision mapping.
Key Methodology: EvoPress (GitHub/HF)
| File Name | Target BPW | VRAM Fit | Optimization Strategy |
|---|---|---|---|
| Qwen-3.5-27B-Opus-Reasoning-Abliterated-EP3.5_bpw.gguf | 3.5 | 12GB - 16GB | Q3_K Base + F32 Mamba/Norms |
| Qwen-3.5-27B-Opus-Reasoning-Abliterated-EP4.25_bpw.gguf | 4.25 | 16GB - 24GB | Q4_K Base + F32 Mamba/Norms |
| Qwen-3.5-27B-Opus-Reasoning-Abliterated-EP5.0_bpw.gguf | 5.0 | 24GB+ | Q5_K Base + F32 Mamba/Norms |
📊 Standard/Traditional Quantizations
Included for comparison and compatibility with older hardware.
| File | Type | Size |
|---|---|---|
| Qwen-3.5-27B-Opus-Reasoning-Abliterated-Q2_K.gguf | Q2_K | ~10 GB |
| Qwen-3.5-27B-Opus-Reasoning-Abliterated-Q3_K.gguf | Q3_K | ~13 GB |
| Qwen-3.5-27B-Opus-Reasoning-Abliterated-Q4_K.gguf | Q4_K | ~16 GB |
| Qwen-3.5-27B-Opus-Reasoning-Abliterated-Q5_K.gguf | Q5_K | ~19 GB |
| Qwen-3.5-27B-Opus-Reasoning-Abliterated-Q6_K.gguf | Q6_K | ~23 GB |
🛠️ Technical Details & Setup
- Architecture: Hybrid Mamba-Transformer (Qwen 3.5)
- Quantization: Performed using a modified
gptq-gguf-toolkitfor Mamba-aware layer mapping. - Requirements: Use a recent build of
llama.cpp(March 2026+) for full Hybrid Mamba support.
🤝 Credits & Acknowledgements
- Jackrong: For the Claude-4.6-Opus reasoning distillation methodology and published model.
- Orion-Zhen: For the abliteration and refusal-removal pipeline.
- Alibaba Qwen Team: For the base Qwen 3.5 architecture.
- Flakily6416: Quantization, layer-mapping, and Mixed-Precision optimization.
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Model tree for Flakily6416/Qwen3.5-27B-Opus-Reasoning-v2-Abliterated-EvoPress-GGUF
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