Instructions to use feder-cr/jev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use feder-cr/jev 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 feder-cr/jev:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf feder-cr/jev:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf feder-cr/jev:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf feder-cr/jev:Q4_K_M

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 feder-cr/jev:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf feder-cr/jev:Q4_K_M

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 feder-cr/jev:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf feder-cr/jev:Q4_K_M

Use Docker

docker model run hf.co/feder-cr/jev:Q4_K_M

LM Studio
Jan
Ollama
How to use feder-cr/jev with Ollama:
```
ollama run hf.co/feder-cr/jev:Q4_K_M
```
Unsloth Desktop

How to use feder-cr/jev with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf feder-cr/jev:Q4_K_M

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": "feder-cr/jev:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use feder-cr/jev with Docker Model Runner:
```
docker model run hf.co/feder-cr/jev:Q4_K_M
```

Lemonade

How to use feder-cr/jev with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull feder-cr/jev:Q4_K_M

Run and chat with the model

lemonade run user.jev-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use feder-cr/jev with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf feder-cr/jev:Q4_K_M

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 feder-cr/jev:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use feder-cr/jev with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf feder-cr/jev:Q4_K_M

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 "feder-cr/jev:Q4_K_M" \
  --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"

jevos-v4

Decisions on a laptop CPU in 28–130 ms. Send a text and a question (yes/no, multiple choice or a score) and get back the probability of each answer, in TypeSafe Jev's API, on your own machine. Free and open source: an alternative to Jev that runs locally.

Same Snake game, same questions for both: jevos-v4 on a laptop answers in about 20 ms, Jev's cloud API in about 350 ms (mostly the network round trip), so the local snake makes many more moves in the same time.

Files

File	What it is
`model/`	the OpenVINO INT8 model that the `jev` binary runs (CPU)
`jevos-v4-q4_k_m.gguf`	the same model as 4-bit GGUF, for llama.cpp and the tools built on it
`jevos-v4-q8_0.gguf`	the same model as 8-bit GGUF

These are the same files as the jevos-v4 release on GitHub; the SHA-256 of each is in that release's SHA256SUMS.txt.

Quickstart

Get the jev binary for your system from the GitHub release (Windows, Linux, macOS on Apple silicon), then put the model next to it:

tar -xzf jev-linux-x64.tar.gz                 # Windows: unzip jev-windows-x64.zip
hf download feder-cr/jev --include "model/*" --local-dir jev
cd jev
./jev serve                                   # Windows: jev.exe serve

curl http://127.0.0.1:8017/v1/systemone -H 'Content-Type: application/json' -d '{
  "model": "jev-latest",
  "state": "I was charged twice for the same order.",
  "questions": {"billing": {"type": "noul", "instructions": "Is this a billing problem?"}}}'

{"model": "jevos-v4", "answers": {"billing": {"type": "noul", "noul": 0.94}}, "usage": {"input_tokens": 27, "output_tokens": 0}}

One binary, CPU only, no Python.

Benchmarks

Latency on a short / long request: jevos-v4 28 / 130 ms; Jev 311 / 314 ms; Qwen3.5-4B 3,060 / 4,761 ms; Laya 129 / 480 ms. Accuracy on 6 tasks (Admission policy, Rental policy, Rules and scenarios, Authority rules, Fraud points, Patent phrases): jevos-v4 0.95, 0.76, 0.76, 0.81, 0.50, 0.37; Jev 1.00, 0.91, 0.88, 0.98, 0.69, 0.59; Qwen3.5-4B 0.82, 0.60, 0.72, 0.78, 0.45, 0.18; Laya 0.54, 0.31, 0.55, 0.64, 0.25, 0.32

Same questions for every system, through the same HTTP client. Latency is the median of 10 requests on an Intel Core Ultra 7 255H laptop, 16 threads, each text read from scratch. No task text was used to train jevos; five of the six sets helped choose the released checkpoint.

	jevos-v4	Jev	Laya
Yes/no questions	✓	✓	✓
Multiple choice	✓	✓	✓
Scores	✓ (early)	✓	✓
Runs on	your machine	cloud	your machine
Cost	free	per token	free
Context	8,192 tokens	not stated	512 tokens

jevos-v4 is much faster than the cloud API and free, but less accurate than Jev on the harder tasks above (fraud points, patent phrases): check it on your own questions before relying on it.

API

POST /v1/systemone, in TypeSafe Jev's wire format: code written for Jev's SDK works unchanged for yes/no questions. One request can ask several questions about the same text, which is read once:

{
  "model": "jev-latest",
  "state": {"item": "wireless mouse", "customer_message": "The box arrived empty. This is the second time!"},
  "questions": {
    "refund": {"type": "noul", "instructions": "Our policy refunds items reported missing within 30 days of delivery. Should this customer get a refund?"},
    "team": {"type": "choice", "instructions": "Which team should handle this message?",
             "criteria": {"billing": "payments, refunds", "shipping": "deliveries, missing parcels", "tech": "a product that does not work"}},
    "anger": {"type": "score", "instructions": "How angry is the customer?", "criteria": ["calm", "annoyed", "angry", "furious"]}
  }
}

A noul returns P(yes); a choice the most probable option; a score the expected level. Choices and scores also return a probability per answer and a confidence: when it is low, send the case to a person. Put the rule in the question, and do sums in code.

Options, memory use and building from source are in the README on GitHub; guides and measurements are in the wiki.

License and authors

MIT. Built by Federico Elia (@feder-cr) together with Loris Salsi (@LosaLosSantos).

Downloads last month: 125

GGUF

Model size

0.9B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

View +1 variant