Instructions to use inclusionAI/Ling-3.0-tiny-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 inclusionAI/Ling-3.0-tiny-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 inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M
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 inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M
Use Docker
docker model run hf.co/inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- vLLM
How to use inclusionAI/Ling-3.0-tiny-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inclusionAI/Ling-3.0-tiny-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": "inclusionAI/Ling-3.0-tiny-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M
- Ollama
How to use inclusionAI/Ling-3.0-tiny-GGUF with Ollama:
ollama run hf.co/inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use inclusionAI/Ling-3.0-tiny-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf inclusionAI/Ling-3.0-tiny-GGUF: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": "inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use inclusionAI/Ling-3.0-tiny-GGUF with Docker Model Runner:
docker model run hf.co/inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M
- Lemonade
How to use inclusionAI/Ling-3.0-tiny-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Ling-3.0-tiny-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use inclusionAI/Ling-3.0-tiny-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 inclusionAI/Ling-3.0-tiny-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 inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use inclusionAI/Ling-3.0-tiny-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf inclusionAI/Ling-3.0-tiny-GGUF: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 "inclusionAI/Ling-3.0-tiny-GGUF: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"
Q4_K_M have tensors quantized using the legacy Q5_0 ?
Hi, I ported Ling 3 Tiny to my inference engine for the JVM ... and noticed that the official GGUF (Q4_K_M) have tensors quantized to Q5_0.
e.g. https://huggingface.co/inclusionAI/Ling-3.0-tiny-GGUF/tree/main?show_file_info=Ling-3.0-tiny-Q4_K_M.gguf , see the blk.7.attn_k_b.weight tensor.
I think this is an honest mistake in the encoding to GGUF,.Q5_0 is a legacy quant with really poor support in the inference engines,, thus poor performance and worse precision ... those tensors should be encoded using Q4_K or Q6_K.
Please consider fixing this and updating the GGUF Q4_K_M quant, it only takes two minutes minutes, and will make the GGUF Q4_K_M quant pristine.
Otherwise, the model is lit ... I've had a great time playing with it, congrats on the release!
Found the culprit: Q4_K requires the inner dim to be a multiple of 256 ... and the quantization falls back Q5_0 otherwise.