Instructions to use GabForge/GabForge-Mini-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use GabForge/GabForge-Mini-v1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="GabForge/GabForge-Mini-v1-GGUF", filename="GabForge-Mini-v1-Q4_K_M.gguf", )
llm.create_chat_completion( 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" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use GabForge/GabForge-Mini-v1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf GabForge/GabForge-Mini-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf GabForge/GabForge-Mini-v1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf GabForge/GabForge-Mini-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf GabForge/GabForge-Mini-v1-GGUF: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 GabForge/GabForge-Mini-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf GabForge/GabForge-Mini-v1-GGUF: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 GabForge/GabForge-Mini-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf GabForge/GabForge-Mini-v1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/GabForge/GabForge-Mini-v1-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use GabForge/GabForge-Mini-v1-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "GabForge/GabForge-Mini-v1-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": "GabForge/GabForge-Mini-v1-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/GabForge/GabForge-Mini-v1-GGUF:Q4_K_M
- Ollama
How to use GabForge/GabForge-Mini-v1-GGUF with Ollama:
ollama run hf.co/GabForge/GabForge-Mini-v1-GGUF:Q4_K_M
- Unsloth Studio
How to use GabForge/GabForge-Mini-v1-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for GabForge/GabForge-Mini-v1-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for GabForge/GabForge-Mini-v1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for GabForge/GabForge-Mini-v1-GGUF to start chatting
- Pi
How to use GabForge/GabForge-Mini-v1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf GabForge/GabForge-Mini-v1-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "GabForge/GabForge-Mini-v1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use GabForge/GabForge-Mini-v1-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf GabForge/GabForge-Mini-v1-GGUF: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 GabForge/GabForge-Mini-v1-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use GabForge/GabForge-Mini-v1-GGUF with Docker Model Runner:
docker model run hf.co/GabForge/GabForge-Mini-v1-GGUF:Q4_K_M
- Lemonade
How to use GabForge/GabForge-Mini-v1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull GabForge/GabForge-Mini-v1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.GabForge-Mini-v1-GGUF-Q4_K_M
List all available models
lemonade list
| license: apache-2.0 | |
| tags: | |
| - gabforge | |
| - vision | |
| - coding | |
| - screenshot-to-code | |
| - qwen3.5 | |
| - gguf | |
| base_model: Qwen/Qwen3.5-9B | |
| pipeline_tag: image-text-to-text | |
| # GabForge Mini v1 — Vision + Coding GGUF | |
| The first GabForge model: a fine-tuned **Qwen3.5-9B** with built-in vision capability, optimized for screenshot→code generation. | |
| ## Model Details | |
| | Property | Value | | |
| |----------|-------| | |
| | Base Model | Qwen3.5-9B (multimodal) | | |
| | Fine-tuning | QLoRA (rank 32), 7,419 WebSight screenshot→code examples | | |
| | Training | 2.15 epochs, final loss 0.15 | | |
| | Quantization | Q4_K_M | | |
| | File Size | 5.3 GB | | |
| | Min VRAM | 8 GB | | |
| | License | Apache 2.0 | | |
| ## Capabilities | |
| - **Screenshot→Code**: Give it a UI screenshot, get HTML/CSS/JS back | |
| - **General Coding**: Inherits Qwen3.5-9B's strong coding ability | |
| - **Vision Understanding**: Reads UI layouts, diagrams, charts, error screenshots | |
| - **Chat**: Standard instruction-following conversational model | |
| ## Usage | |
| Works with any llama.cpp-compatible inference engine: | |
| ```bash | |
| # With llama-server | |
| llama-server -m GabForge-Mini-v1-Q4_K_M.gguf --port 8766 | |
| # With GabForge AI Studio (automatic) | |
| # Download via Settings → AI Models → Local tab | |
| ``` | |
| ## Made for GabForge AI Studio | |
| This model is the default local model in [GabForge AI Studio](https://gabforge.ai) — the privacy-first AI coding IDE. Vision runs entirely on your machine. | |
| ## Training Data | |
| - [HuggingFaceM4/WebSight](https://huggingface.co/datasets/HuggingFaceM4/WebSight) — screenshot→HTML/CSS pairs | |
| - Additional coding data from Qwen3.5-9B's base knowledge | |