Text Generation
GGUF
llama.cpp
ternary
2-bit
llama-cpp
cuda
metal
on-device
hybrid-attention
prismml
bonsai
qwen3.5
abliterated
uncensored
crack
conversational
Instructions to use dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use dealignai/Bonsai-2-27B-Ternary-CRACK-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 dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0 # Run inference directly in the terminal: llama cli -hf dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0 # Run inference directly in the terminal: llama cli -hf dealignai/Bonsai-2-27B-Ternary-CRACK-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 dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0 # Run inference directly in the terminal: ./llama-cli -hf dealignai/Bonsai-2-27B-Ternary-CRACK-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 dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0
Use Docker
docker model run hf.co/dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0
- LM Studio
- Jan
- vLLM
How to use dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dealignai/Bonsai-2-27B-Ternary-CRACK-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": "dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0
- Ollama
How to use dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF with Ollama:
ollama run hf.co/dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0
- Unsloth Desktop
- Pi
How to use dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF with Docker Model Runner:
docker model run hf.co/dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0
- Lemonade
How to use dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0
Run and chat with the model
lemonade run user.Bonsai-2-27B-Ternary-CRACK-GGUF-Q2_0
List all available models
lemonade list
- Hermes Agent
How to use dealignai/Bonsai-2-27B-Ternary-CRACK-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 dealignai/Bonsai-2-27B-Ternary-CRACK-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 dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF:Q2_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use dealignai/Bonsai-2-27B-Ternary-CRACK-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf dealignai/Bonsai-2-27B-Ternary-CRACK-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 "dealignai/Bonsai-2-27B-Ternary-CRACK-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"
MMLU per-subject table (n=2280 40-per-subject)
Browse files
README.md
CHANGED
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| illegal | 53 | 90.6% | **0.0%** | 9.4% | **100.0%** |
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| misinformation_disinformation | 54 | 96.3% | **1.9%** | 3.7% | **98.1%** |
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### MMLU (
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| build | acc | Δ |
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| Base `PQ2_0` |
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| **CRACK `PQ2_0`** | **
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CRACK preserves
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<details>
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<summary><b>Per-subject
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</details>
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| illegal | 53 | 90.6% | **0.0%** | 9.4% | **100.0%** |
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| misinformation_disinformation | 54 | 96.3% | **1.9%** | 3.7% | **98.1%** |
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### MMLU (n=2,280 = 40 questions × 57 subjects, next-token letter-logit)
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| build | acc | Δ |
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| Base `PQ2_0` | 39.82% | — |
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| **CRACK `PQ2_0`** | **39.74%** | **-0.08 pp** |
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CRACK preserves general capability — Δ within noise on the 40-per-subject sample.
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<details>
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<summary><b>Per-subject accuracy (all 57 subjects)</b></summary>
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| subject | base | CRACK | Δpp | n |
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| abstract_algebra | 30.0% | 30.0% | +0.0 | 40 |
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| anatomy | 30.0% | 42.5% | +12.5 | 40 |
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| astronomy | 40.0% | 55.0% | +15.0 | 40 |
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| business_ethics | 37.5% | 50.0% | +12.5 | 40 |
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| clinical_knowledge | 40.0% | 40.0% | +0.0 | 40 |
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| college_biology | 50.0% | 45.0% | -5.0 | 40 |
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| college_chemistry | 25.0% | 35.0% | +10.0 | 40 |
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| college_computer_science | 45.0% | 52.5% | +7.5 | 40 |
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| college_mathematics | 32.5% | 35.0% | +2.5 | 40 |
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| college_medicine | 27.5% | 40.0% | +12.5 | 40 |
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| college_physics | 30.0% | 40.0% | +10.0 | 40 |
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| computer_security | 50.0% | 37.5% | -12.5 | 40 |
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| conceptual_physics | 40.0% | 37.5% | -2.5 | 40 |
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| econometrics | 32.5% | 50.0% | +17.5 | 40 |
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| electrical_engineering | 37.5% | 47.5% | +10.0 | 40 |
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| elementary_mathematics | 45.0% | 52.5% | +7.5 | 40 |
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| formal_logic | 32.5% | 35.0% | +2.5 | 40 |
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| global_facts | 35.0% | 22.5% | -12.5 | 40 |
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| high_school_biology | 35.0% | 15.0% | -20.0 | 40 |
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| high_school_chemistry | 52.5% | 30.0% | -22.5 | 40 |
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| high_school_computer_science | 47.5% | 52.5% | +5.0 | 40 |
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| high_school_european_history | 42.5% | 40.0% | -2.5 | 40 |
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| high_school_geography | 30.0% | 45.0% | +15.0 | 40 |
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| high_school_government_and_politics | 52.5% | 47.5% | -5.0 | 40 |
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| high_school_macroeconomics | 37.5% | 47.5% | +10.0 | 40 |
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| high_school_mathematics | 32.5% | 32.5% | +0.0 | 40 |
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| high_school_microeconomics | 35.0% | 37.5% | +2.5 | 40 |
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| high_school_physics | 22.5% | 40.0% | +17.5 | 40 |
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| high_school_psychology | 45.0% | 40.0% | -5.0 | 40 |
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| high_school_statistics | 42.5% | 40.0% | -2.5 | 40 |
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| high_school_us_history | 55.0% | 40.0% | -15.0 | 40 |
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| high_school_world_history | 50.0% | 37.5% | -12.5 | 40 |
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| human_aging | 45.0% | 42.5% | -2.5 | 40 |
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| human_sexuality | 42.5% | 32.5% | -10.0 | 40 |
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| international_law | 72.5% | 50.0% | -22.5 | 40 |
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| jurisprudence | 40.0% | 45.0% | +5.0 | 40 |
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| logical_fallacies | 37.5% | 47.5% | +10.0 | 40 |
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| machine_learning | 35.0% | 35.0% | +0.0 | 40 |
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| management | 47.5% | 37.5% | -10.0 | 40 |
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| marketing | 42.5% | 57.5% | +15.0 | 40 |
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| medical_genetics | 52.5% | 62.5% | +10.0 | 40 |
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| miscellaneous | 42.5% | 37.5% | -5.0 | 40 |
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| moral_disputes | 40.0% | 22.5% | -17.5 | 40 |
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| moral_scenarios | 17.5% | 27.5% | +10.0 | 40 |
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| nutrition | 42.5% | 55.0% | +12.5 | 40 |
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| philosophy | 42.5% | 35.0% | -7.5 | 40 |
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| prehistory | 35.0% | 27.5% | -7.5 | 40 |
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| professional_accounting | 20.0% | 25.0% | +5.0 | 40 |
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| professional_law | 40.0% | 22.5% | -17.5 | 40 |
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| professional_medicine | 27.5% | 32.5% | +5.0 | 40 |
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| professional_psychology | 47.5% | 32.5% | -15.0 | 40 |
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| public_relations | 17.5% | 22.5% | +5.0 | 40 |
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| security_studies | 47.5% | 40.0% | -7.5 | 40 |
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| sociology | 57.5% | 47.5% | -10.0 | 40 |
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| us_foreign_policy | 50.0% | 45.0% | -5.0 | 40 |
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| virology | 30.0% | 30.0% | +0.0 | 40 |
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| world_religions | 57.5% | 60.0% | +2.5 | 40 |
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</details>
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