Instructions to use espetro/kev-0.8b-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 espetro/kev-0.8b-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 espetro/kev-0.8b-gguf:Q8_0 # Run inference directly in the terminal: llama cli -hf espetro/kev-0.8b-gguf:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf espetro/kev-0.8b-gguf:Q8_0 # Run inference directly in the terminal: llama cli -hf espetro/kev-0.8b-gguf:Q8_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 espetro/kev-0.8b-gguf:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf espetro/kev-0.8b-gguf:Q8_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 espetro/kev-0.8b-gguf:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf espetro/kev-0.8b-gguf:Q8_0
Use Docker
docker model run hf.co/espetro/kev-0.8b-gguf:Q8_0
- LM Studio
- Jan
- Ollama
How to use espetro/kev-0.8b-gguf with Ollama:
ollama run hf.co/espetro/kev-0.8b-gguf:Q8_0
- Unsloth Desktop
- Pi
How to use espetro/kev-0.8b-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf espetro/kev-0.8b-gguf:Q8_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": "espetro/kev-0.8b-gguf:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use espetro/kev-0.8b-gguf with Docker Model Runner:
docker model run hf.co/espetro/kev-0.8b-gguf:Q8_0
- Lemonade
How to use espetro/kev-0.8b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull espetro/kev-0.8b-gguf:Q8_0
Run and chat with the model
lemonade run user.kev-0.8b-gguf-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use espetro/kev-0.8b-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 espetro/kev-0.8b-gguf:Q8_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 espetro/kev-0.8b-gguf:Q8_0
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use espetro/kev-0.8b-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf espetro/kev-0.8b-gguf:Q8_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 "espetro/kev-0.8b-gguf:Q8_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"
Kev 0.8B โ packed GGUF (q8_0)
Kev-0.8B is a Jev-style "System One" decision model: a Qwen3.5-0.8B backbone plus a trained pointer head that returns calibrated probabilities for typed questions instead of generating text.
This GGUF has the pointer head and calibration temperature baked in
(dec.head_* tensors + kev.* metadata), produced by
espetro/llama.cpp's tools/kev/kev_pack.py
from the taigrr/kev-0.8b-gguf
gojev bundle (jaredpalmer/kev-0.8b checkpoint).
Use with the Kev-enabled llama.cpp fork
llama-server -hf espetro/kev-0.8b-gguf
# -> serves TypeSafe /v1/systemone + /studio automatically
curl localhost:8080/v1/systemone -H 'content-type: application/json' -d '{
"state": "Shoes arrived two weeks late and in the wrong size.",
"questions": {
"department": {"type": "choice", "instructions": "Which team handles this?",
"criteria": {"returns": "Exchanges, refunds", "shipping": "Delays, lost packages"}}
}
}'
llama-decide -hf espetro/kev-0.8b-gguf --json request.json
The file still loads in stock llama.cpp as an ordinary Qwen3.5 model โ the
kev.* metadata and head tensors are simply ignored there.
In the browser
espetro.github.io/llama.cpp lazy-loads this file into a llama.cpp WASM build (needs ~1 GB live, q8_0 is the only recommended browser quant โ q4 drifts enough to break the calibration).
Source & license
- Weights + head: Apache-2.0 โ jaredpalmer/kev
- Source bundle: taigrr/kev-0.8b-gguf (gojev, 0BSD)
- Pack/quantize + runtime: espetro/llama.cpp
kevbranch (MIT)
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