Instructions to use unsloth/flint-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 unsloth/flint-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 unsloth/flint-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/flint-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/flint-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/flint-GGUF:UD-Q4_K_XL
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf unsloth/flint-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama cli -hf unsloth/flint-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/flint-GGUF:UD-Q4_K_XL
- Llama new
- LM Studio
- Jan
- vLLM
How to use unsloth/flint-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/flint-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": "unsloth/flint-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/unsloth/flint-GGUF:UD-Q4_K_XL
- Ollama
How to use unsloth/flint-GGUF with Ollama:
ollama run hf.co/unsloth/flint-GGUF:UD-Q4_K_XL
- Unsloth Desktop
- Pi
How to use unsloth/flint-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/flint-GGUF:UD-Q4_K_XL
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": "unsloth/flint-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use unsloth/flint-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/flint-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/flint-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/flint-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.flint-GGUF-UD-Q4_K_XL
List all available models
lemonade list
- Hermes Agent
How to use unsloth/flint-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 unsloth/flint-GGUF:UD-Q4_K_XL
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 unsloth/flint-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use unsloth/flint-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/flint-GGUF:UD-Q4_K_XL
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 "unsloth/flint-GGUF:UD-Q4_K_XL" \ --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"
RecursiveSI/flint-int4-gguf vs UD
Hi
How does UD 4 bit XL compare to RecursiveSI/flint-int4-gguf in accuracy?
I would rather like to know the comparison to the nvfp4 gguf
Quantization. After training, our Eureka Machine searched for quantization recipes that shrink Flint’s memory footprint while preserving its capabilities. Against common practice, it quantized the output layer (lm_head) to 4 bits. Because Flint has only 2.5 billion active parameters, its output layer is a large share of the weights read for each token. To recover accuracy, it added an offline distillation stage in which the quantized model trains on its own pre-sampled responses from a self-discovered coreset, with the 16-bit model as teacher. This nearly halved the number of training steps compared with our quantization-aware training baseline. The final NVFP4 model scores within 0.7 points of the 16-bit model across our benchmarks.