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 Desktop
- 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 @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": "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"
| { | |
| "all_terminal_statistics_present": true, | |
| "bf16_self_kld_floor": 0.0, | |
| "runs": { | |
| "BF16": { | |
| "chunks": 32, | |
| "context": 512, | |
| "excess_mean_kld_nats_above_bf16_floor": 0.0, | |
| "ln_ppl_ratio": { | |
| "mean": 0.002791, | |
| "se": 0.001078 | |
| }, | |
| "log": "kld-BF16.log", | |
| "mean_kld_nats": { | |
| "mean": 0.0, | |
| "se": 0.0 | |
| }, | |
| "ppl_base": { | |
| "mean": 11.868127, | |
| "se": 0.412222 | |
| }, | |
| "ppl_difference": { | |
| "mean": 0.033176, | |
| "se": 0.013026 | |
| }, | |
| "ppl_q": { | |
| "mean": 11.901303, | |
| "se": 0.415179 | |
| }, | |
| "ppl_ratio": { | |
| "mean": 1.002795, | |
| "se": 0.001081 | |
| }, | |
| "rms_delta_p_percent": { | |
| "mean": 0.001, | |
| "se": 0.0 | |
| }, | |
| "same_top_p_percent": { | |
| "mean": 99.975, | |
| "se": 0.017 | |
| }, | |
| "suspicious_failure_patterns": [], | |
| "terminal_statistics_present": true | |
| }, | |
| "Q4_K_M": { | |
| "chunks": 32, | |
| "context": 512, | |
| "excess_mean_kld_nats_above_bf16_floor": 0.130069, | |
| "ln_ppl_ratio": { | |
| "mean": 0.063922, | |
| "se": 0.007733 | |
| }, | |
| "log": "kld-Q4_K_M.log", | |
| "mean_kld_nats": { | |
| "mean": 0.130069, | |
| "se": 0.003051 | |
| }, | |
| "ppl_base": { | |
| "mean": 11.868127, | |
| "se": 0.412222 | |
| }, | |
| "ppl_difference": { | |
| "mean": 0.783402, | |
| "se": 0.100802 | |
| }, | |
| "ppl_q": { | |
| "mean": 12.651529, | |
| "se": 0.447483 | |
| }, | |
| "ppl_ratio": { | |
| "mean": 1.066009, | |
| "se": 0.008243 | |
| }, | |
| "rms_delta_p_percent": { | |
| "mean": 10.016, | |
| "se": 0.244 | |
| }, | |
| "same_top_p_percent": { | |
| "mean": 85.221, | |
| "se": 0.393 | |
| }, | |
| "suspicious_failure_patterns": [], | |
| "terminal_statistics_present": true | |
| }, | |
| "Q4_K_M_NO_IMATRIX": { | |
| "chunks": 32, | |
| "context": 512, | |
| "excess_mean_kld_nats_above_bf16_floor": 0.131547, | |
| "ln_ppl_ratio": { | |
| "mean": 0.040432, | |
| "se": 0.007836 | |
| }, | |
| "log": "kld-Q4_K_M-no-imatrix.log", | |
| "mean_kld_nats": { | |
| "mean": 0.131547, | |
| "se": 0.003047 | |
| }, | |
| "ppl_base": { | |
| "mean": 11.868127, | |
| "se": 0.412222 | |
| }, | |
| "ppl_difference": { | |
| "mean": 0.489689, | |
| "se": 0.096967 | |
| }, | |
| "ppl_q": { | |
| "mean": 12.357816, | |
| "se": 0.432376 | |
| }, | |
| "ppl_ratio": { | |
| "mean": 1.041261, | |
| "se": 0.008159 | |
| }, | |
| "rms_delta_p_percent": { | |
| "mean": 10.104, | |
| "se": 0.252 | |
| }, | |
| "same_top_p_percent": { | |
| "mean": 84.596, | |
| "se": 0.4 | |
| }, | |
| "suspicious_failure_patterns": [], | |
| "terminal_statistics_present": true | |
| } | |
| } | |
| } | |