Instructions to use Roy3838/OneJev-0.8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Roy3838/OneJev-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 Roy3838/OneJev-0.8B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Roy3838/OneJev-0.8B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Roy3838/OneJev-0.8B-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Roy3838/OneJev-0.8B-GGUF:F16

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 Roy3838/OneJev-0.8B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Roy3838/OneJev-0.8B-GGUF:F16

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 Roy3838/OneJev-0.8B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Roy3838/OneJev-0.8B-GGUF:F16

Use Docker

docker model run hf.co/Roy3838/OneJev-0.8B-GGUF:F16

Llama new
LM Studio
Jan

vLLM

How to use Roy3838/OneJev-0.8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Roy3838/OneJev-0.8B-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": "Roy3838/OneJev-0.8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Roy3838/OneJev-0.8B-GGUF:F16

Ollama
How to use Roy3838/OneJev-0.8B-GGUF with Ollama:
```
ollama run hf.co/Roy3838/OneJev-0.8B-GGUF:F16
```
Unsloth Desktop

How to use Roy3838/OneJev-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 Roy3838/OneJev-0.8B-GGUF:F16

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": "Roy3838/OneJev-0.8B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Roy3838/OneJev-0.8B-GGUF with Docker Model Runner:
```
docker model run hf.co/Roy3838/OneJev-0.8B-GGUF:F16
```

Lemonade

How to use Roy3838/OneJev-0.8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Roy3838/OneJev-0.8B-GGUF:F16

Run and chat with the model

lemonade run user.OneJev-0.8B-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use Roy3838/OneJev-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 Roy3838/OneJev-0.8B-GGUF:F16

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 Roy3838/OneJev-0.8B-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Roy3838/OneJev-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 Roy3838/OneJev-0.8B-GGUF:F16

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 "Roy3838/OneJev-0.8B-GGUF:F16" \
  --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"

OneJev-0.8B GGUF

GGUF conversion of OmniJev/OneJev-0.8B, a 0.8B decision model (Qwen3.5-0.8B base), for llama.cpp and the Observer desktop app.

File	Use
`OneJev-0.8B-Q8_0.gguf`	Default, about 1.1 GB
`OneJev-0.8B-F16.gguf`	Reference precision, about 2.0 GB
`mmproj-OneJev-0.8B-F16.gguf`	Vision projector, needed for image (screen/camera) input

How it's used

OneJev is not a chat model. It answers a typed question about a state (text and/or images) by putting probability on the option letters A..Z. Read the logits of those letter tokens at the answer position after a single forward pass; do not sample text.

The prompt format follows the OneJev model card: a system turn, then a <state>…</state> block plus the question and lettered options in the user turn, rendered with the embedded chat template and enable_thinking=false.
Observer builds these prompts in app/src/utils/localLlm/systemOne.ts and scores them natively in app/plugins/llm_engine (LlmEngine::decide).

Conversion

Converted with convert_hf_to_gguf.py from llama.cpp commit e21cdc11a (the build vendored by Observer's llama-cpp-rs). Text model at F16 and Q8_0; vision projector at F16.
The rendered prompt was checked to be byte-identical to transformers.js apply_chat_template for the onnx-community/OneJev-0.8B-ONNX processor.
Smoke-tested in Observer's llama.cpp engine. Q8_0 and F16 agree within 0.004 on a text yes/no probability.

License and intended use follow the base model.