Instructions to use Mike0021/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 Mike0021/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 Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Mike0021/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 Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Mike0021/Ling-3.0-tiny-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 Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Mike0021/Ling-3.0-tiny-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 Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Mike0021/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 "Mike0021/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": "Mike0021/Ling-3.0-tiny-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M
- Ollama
How to use Mike0021/Ling-3.0-tiny-GGUF with Ollama:
ollama run hf.co/Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M
- Unsloth Studio
How to use Mike0021/Ling-3.0-tiny-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 Mike0021/Ling-3.0-tiny-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 Mike0021/Ling-3.0-tiny-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Mike0021/Ling-3.0-tiny-GGUF to start chatting
- Pi
How to use Mike0021/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 Mike0021/Ling-3.0-tiny-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": "Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Mike0021/Ling-3.0-tiny-GGUF with Docker Model Runner:
docker model run hf.co/Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M
- Lemonade
How to use Mike0021/Ling-3.0-tiny-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Mike0021/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 Mike0021/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 Mike0021/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 Mike0021/Ling-3.0-tiny-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Mike0021/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 Mike0021/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 "Mike0021/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"
| { | |
| "candidates": { | |
| "IQ4_NL": { | |
| "bytes": 4526279776, | |
| "execution_passed": true, | |
| "mean_kld_nats": { | |
| "mean": 0.149734, | |
| "standard_error": 0.003381 | |
| }, | |
| "not_published": true, | |
| "ppl": 12.48105, | |
| "ppl_difference": 0.612923, | |
| "reason": "Only 28,606,464 bytes smaller than Q4_K_S while materially worse in KLD; its small KLD gain over the smaller IQ4_XS was not statistically compelling in this screen.", | |
| "same_top_percent": 84.522, | |
| "sha256": "3bc2c89844d293531e074e796cc05e6583d282214b6e43bbba474255705072b0" | |
| }, | |
| "MXFP4_MOE": { | |
| "blackwell_benchmark": { | |
| "artifact_comparison": "Q4_K_S", | |
| "generation_tokens_per_second": { | |
| "MXFP4_MOE": 255.307518, | |
| "Q4_K_S": 277.999794, | |
| "relative_change_percent": -8.162695 | |
| }, | |
| "prompt_tokens_per_second": { | |
| "MXFP4_MOE": 6391.33598, | |
| "Q4_K_S": 5422.155601, | |
| "relative_change_percent": 17.874448 | |
| }, | |
| "raw_report": "bench-Q4_K_S-vs-MXFP4_MOE.json", | |
| "repetitions": 5 | |
| }, | |
| "bytes": 4718248800, | |
| "execution_passed": true, | |
| "mean_kld_nats": { | |
| "mean": 0.267021, | |
| "standard_error": 0.006113 | |
| }, | |
| "not_published": true, | |
| "ppl": 12.708273, | |
| "ppl_difference": 0.840146, | |
| "reason": "Q4_K_S is 163,362,560 bytes smaller and substantially more faithful; MXFP4 improved prompt throughput on this Blackwell GPU but reduced generation throughput.", | |
| "same_top_percent": 79.424, | |
| "sha256": "dd23da2994871320d8c7ac0f29859303b3e1e6a24894056b6503c2459aeddf7b", | |
| "tensor_whitelist_audit": { | |
| "F32": 215, | |
| "MXFP4": 69, | |
| "Q8_0": 242, | |
| "passed": true, | |
| "structure_report": "structure-MXFP4_MOE.json" | |
| } | |
| } | |
| }, | |
| "purpose": "Transparent record of generated candidates omitted from the published model-weight frontier" | |
| } | |