Instructions to use CapitaineJACOB/Stera-AI-Lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use CapitaineJACOB/Stera-AI-Lite 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 CapitaineJACOB/Stera-AI-Lite:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CapitaineJACOB/Stera-AI-Lite:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf CapitaineJACOB/Stera-AI-Lite:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf CapitaineJACOB/Stera-AI-Lite: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 CapitaineJACOB/Stera-AI-Lite:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CapitaineJACOB/Stera-AI-Lite: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 CapitaineJACOB/Stera-AI-Lite:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CapitaineJACOB/Stera-AI-Lite:Q4_K_M

Use Docker

docker model run hf.co/CapitaineJACOB/Stera-AI-Lite:Q4_K_M

LM Studio
Jan
Ollama
How to use CapitaineJACOB/Stera-AI-Lite with Ollama:
```
ollama run hf.co/CapitaineJACOB/Stera-AI-Lite:Q4_K_M
```
Unsloth Desktop

How to use CapitaineJACOB/Stera-AI-Lite with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CapitaineJACOB/Stera-AI-Lite: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": "CapitaineJACOB/Stera-AI-Lite:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use CapitaineJACOB/Stera-AI-Lite with Docker Model Runner:
```
docker model run hf.co/CapitaineJACOB/Stera-AI-Lite:Q4_K_M
```

Lemonade

How to use CapitaineJACOB/Stera-AI-Lite with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CapitaineJACOB/Stera-AI-Lite:Q4_K_M

Run and chat with the model

lemonade run user.Stera-AI-Lite-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use CapitaineJACOB/Stera-AI-Lite with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CapitaineJACOB/Stera-AI-Lite: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 CapitaineJACOB/Stera-AI-Lite:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use CapitaineJACOB/Stera-AI-Lite with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf CapitaineJACOB/Stera-AI-Lite: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 "CapitaineJACOB/Stera-AI-Lite: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"

Stera IA Lite

Modèle personnel entraîné par Fendrix Studio (fine-tuning LoRA, export GGUF via llama.cpp).

Fichiers

model-Stera-Lite-Q4_K_M.gguf : modèle quantifié en Q4_K_M
mmproj-Stera-Lite_V10-Q8.gguf : projecteur de vision, nécessaire pour l'analyse d'images

Utilisation

Dans LM Studio (ou llama.cpp), charge le modèle principal. Garde le fichier mmproj dans le même dossier pour activer la vision.

Modèle de base

Qwen/Qwen3-VL-4B-Instruct (licence Apache 2.0).

Downloads last month: 44

GGUF

Model size

4B params

Architecture

qwen3vl

Hardware compatibility

4-bit

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