Text Classification
GGUF
jev-style
English
jev
openjev
decision-model
typed-decisions
bonsai
ternary
local-inference
blackwell
conversational
Instructions to use ajh-code/Jev-Bonsai-Compass with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- jev-style
How to use ajh-code/Jev-Bonsai-Compass with jev-style:
pip install jev-style # GGUF builds score through llama.cpp: build the jev-score binary once hf download ajh-code/Jev-Bonsai-Compass build_jev_score.sh jev_score.cpp --local-dir jev-score export JEV_SCORE_BIN=$(sh jev-score/build_jev_score.sh /path/to/llama.cpp | tail -n 1)
from jev_style import JevStyle, noul, choice js = JevStyle.from_pretrained("ajh-code/Jev-Bonsai-Compass") out = js.decide("I was charged twice for one order.", { "billing": noul("This message is about billing."), "team": choice("Which team should handle it?", ["billing", "shipping", "tech"]), }) print(out["answers"]["team"]["choice"]) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ajh-code/Jev-Bonsai-Compass 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 ajh-code/Jev-Bonsai-Compass:Q2_0 # Run inference directly in the terminal: llama cli -hf ajh-code/Jev-Bonsai-Compass:Q2_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ajh-code/Jev-Bonsai-Compass:Q2_0 # Run inference directly in the terminal: llama cli -hf ajh-code/Jev-Bonsai-Compass: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 ajh-code/Jev-Bonsai-Compass:Q2_0 # Run inference directly in the terminal: ./llama-cli -hf ajh-code/Jev-Bonsai-Compass: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 ajh-code/Jev-Bonsai-Compass:Q2_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ajh-code/Jev-Bonsai-Compass:Q2_0
Use Docker
docker model run hf.co/ajh-code/Jev-Bonsai-Compass:Q2_0
- LM Studio
- Jan
- Ollama
How to use ajh-code/Jev-Bonsai-Compass with Ollama:
ollama run hf.co/ajh-code/Jev-Bonsai-Compass:Q2_0
- Unsloth Desktop
- Pi
How to use ajh-code/Jev-Bonsai-Compass with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ajh-code/Jev-Bonsai-Compass: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": "ajh-code/Jev-Bonsai-Compass:Q2_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use ajh-code/Jev-Bonsai-Compass with Docker Model Runner:
docker model run hf.co/ajh-code/Jev-Bonsai-Compass:Q2_0
- Lemonade
How to use ajh-code/Jev-Bonsai-Compass with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ajh-code/Jev-Bonsai-Compass:Q2_0
Run and chat with the model
lemonade run user.Jev-Bonsai-Compass-Q2_0
List all available models
lemonade list
- Hermes Agent
How to use ajh-code/Jev-Bonsai-Compass with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ajh-code/Jev-Bonsai-Compass: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 ajh-code/Jev-Bonsai-Compass:Q2_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use ajh-code/Jev-Bonsai-Compass with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf ajh-code/Jev-Bonsai-Compass: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 "ajh-code/Jev-Bonsai-Compass: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"
File size: 2,860 Bytes
b4b0f75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 | [
{
"domain": "news",
"repo": "fancyzhx/ag_news",
"hub_revision": "eb185aade064a813bc0b7f42de02595523103ca4",
"config": "default",
"split": "test",
"total": 7600,
"eligible": 7600,
"sample": 120,
"license": [
"unknown"
],
"transport": "dataset-server full split snapshot; exact viewer source revision unknown"
},
{
"domain": "emotion",
"repo": "dair-ai/emotion",
"hub_revision": "cab853a1dbdf4c42c2b3ef2173804746df8825fe",
"config": "split",
"split": "test",
"total": 2000,
"eligible": 2000,
"sample": 120,
"license": [
"other"
],
"transport": "dataset-server full split snapshot; exact viewer source revision unknown"
},
{
"domain": "reading",
"repo": "google/boolq",
"hub_revision": "35b264d03638db9f4ce671b711558bf7ff0f80d5",
"config": "default",
"split": "validation",
"total": 3270,
"eligible": 3270,
"sample": 120,
"license": [
"cc-by-sa-3.0"
],
"transport": "dataset-server full split snapshot; exact viewer source revision unknown"
},
{
"domain": "entailment",
"repo": "stanfordnlp/snli",
"hub_revision": "cdb5c3d5eed6ead6e5a341c8e56e669bb666725b",
"config": "plain_text",
"split": "test",
"total": 10000,
"eligible": 9824,
"sample": 120,
"license": [
"cc-by-sa-4.0"
],
"transport": "pinned Hub revision Parquet"
},
{
"domain": "science",
"repo": "allenai/ai2_arc",
"hub_revision": "210d026faf9955653af8916fad021475a3f00453",
"config": "ARC-Challenge",
"split": "test",
"total": 1172,
"eligible": 1172,
"sample": 120,
"license": [
"cc-by-sa-4.0"
],
"transport": "pinned Hub revision Parquet"
},
{
"domain": "banking20",
"repo": "PolyAI/banking77",
"upstream_url": "https://raw.githubusercontent.com/PolyAI-LDN/task-specific-datasets/57ec275d8078af65b7731c2a98be812d844a6d6b/banking_data/test.csv",
"revision": "57ec275d8078af65b7731c2a98be812d844a6d6b",
"total": 3080,
"sample": 120,
"classes": [
"Refund_not_showing_up",
"balance_not_updated_after_cheque_or_cash_deposit",
"card_arrival",
"card_payment_not_recognised",
"change_pin",
"compromised_card",
"declined_card_payment",
"declined_cash_withdrawal",
"edit_personal_details",
"exchange_charge",
"failed_transfer",
"getting_virtual_card",
"lost_or_stolen_phone",
"passcode_forgotten",
"reverted_card_payment?",
"supported_cards_and_currencies",
"terminate_account",
"top_up_by_cash_or_cheque",
"top_up_reverted",
"unable_to_verify_identity"
],
"license": [
"cc-by-4.0"
],
"note": "20 of 77 intents, six test examples per intent; not full Banking77"
}
] |