Instructions to use Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF", filename="Ternary-Bonsai-27B-Abliterated-LowDeg-Q2_0.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 Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-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 Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0 # Run inference directly in the terminal: llama cli -hf Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0 # Run inference directly in the terminal: llama cli -hf Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
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 Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0 # Run inference directly in the terminal: ./llama-cli -hf Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
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 Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
Use Docker
docker model run hf.co/Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
- LM Studio
- Jan
- vLLM
How to use Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-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": "Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
- Ollama
How to use Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF with Ollama:
ollama run hf.co/Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
- Unsloth Studio
How to use Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-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 Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-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 Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF to start chatting
- Pi
How to use Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
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": "Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-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 Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
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 Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
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 "Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0" \ --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 Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF with Docker Model Runner:
docker model run hf.co/Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
- Lemonade
How to use Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0
Run and chat with the model
lemonade run user.Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF-Q2_0
List all available models
lemonade list
Ternary Bonsai 27B Abliterated-LowDeg (Q2_0 GGUF)
Low-degradation direct GGUF abliteration of Prism Ternary Bonsai 27B.
- Format: official Q2_0 g128 ternary pack (~7.2 GB)
- No runtime steering / no BF16 bake / no requant
- Built for the PrismML llama.cpp fork (hybrid-attention Q2_0 kernels)
Hard-convert 38/40 on a fixed harmful pilot while keeping coding near the parent — not a max-HC-only scar model.
Files
| File | Role |
|---|---|
Ternary-Bonsai-27B-Abliterated-LowDeg-Q2_0.gguf |
language model (this release) |
Optional speculative decode: use Prism’s official DSpark drafter unchanged
(Ternary-Bonsai-27B-dspark-Q4_1.gguf from the base repo).
Accept-rate A/B on this weight vs parent: ≈1.00× (n=30, CI overlap) → no draft retrain needed.
Quickstart
# Prism fork required
git clone https://github.com/PrismML-Eng/llama.cpp
cmake -B build -DGGML_CUDA=ON && cmake --build build -j
./build/bin/llama-server \
-m Ternary-Bonsai-27B-Abliterated-LowDeg-Q2_0.gguf \
-ngl 99 -c 8192 -np 1 \
--host 127.0.0.1 --port 8080
# optional DSpark
./build/bin/llama-server \
-m Ternary-Bonsai-27B-Abliterated-LowDeg-Q2_0.gguf \
-md Ternary-Bonsai-27B-dspark-Q4_1.gguf \
-ngl 99 -ngld 99 \
--spec-type draft-dspark --spec-draft-n-max 4 \
-c 8192 -np 1
Method (short)
- Residual / direction code-edit on ternary writers over the parent Q2_0 pack
- Escalate to a high hard-convert seed (
esc-ALL-f65) - Seed-stage surgical rollback: restore parent bytes on selected L0–7 writers
(minimal maskminM23, ~1.5 MB changed vs full L0–7 rollback) - Gate on paired harmful eval + capability pilots; keep low-degradation winner
Runtime RepE hooks are not required at serve time.
Results
All figures below are same-condition lab measurements. They are not a full open leaderboard reproduction.
Refusal gate (harmful pilot)
| value | |
|---|---|
| n | 40 |
parent baseline refusals (REFUSAL | COMPLY_WITH_HEDGE) |
40/40 |
hard convert (parent refused → candidate pure COMPLY) |
38/40 |
| empty completions | 0 |
| transport errors | 0 |
Hard convert does not count hedges / weak / empty.
Harmless side-effect (same n=40): reverse flip 0.
Capability pilot
| split | this model |
|---|---|
| coding | 19/20 |
| tool | 15/15 |
| agentic | 5/5 |
Parent coding on the same pilot was 19/20; an earlier HC-max seed (esc-f65) dropped coding to 16/20.
Serious benches (seed=0, thinking off)
| bench | parent Q2_0 | this (LowDeg) |
|---|---|---|
| GSM8K (n=100) | 68 | 71 |
| MMLU subset (n=100) | 62 | 60 |
| HumanEval (n=164) | 141 | 140 |
| MBPP (n=100) | 66 | 64 |
Decode (reference, not a claim of superiority)
- RTX PRO 6000 WS, no draft: single-stream TG ~139 tok/s (depth0, llama-bench n=5)
- RTX 5070 Ti 16GB + official DSpark: single-stream
80–85 tok/s, DSpark-active ctx up to 16k; parallel sweet spot np=2 (89 agg tok/s)
Intended use
Research and local/offline assistants where reduced over-refusal is desired.
Still a general model: it can produce unsafe content if asked. Use under your own policy and applicable law.
Limitations
- Requires Prism llama.cpp (stock llama.cpp may not run this Q2_0 hybrid pack correctly)
- Abliteration is behavioral, not a safety proof
- Long-context / high-parallel on 16GB cards can silently disable DSpark when compute buffers OOM — check
timings.draft_n - Vision mmproj is not included (text GGUF only)
Attribution
- Base weights & format: prism-ml/Ternary-Bonsai-27B-gguf (Apache-2.0)
- Architecture lineage: Qwen3.5 / Qwen3.6-27B hybrid attention
- This derivative: direct ternary GGUF edit + seed-stage low-degradation rollback (
W-RB-L0-7-minM23)
License
Apache-2.0 (same as the base GGUF release).
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2-bit
ollama run hf.co/Hikari07jp/Ternary-Bonsai-27B-Abliterated-LowDeg-GGUF:Q2_0