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"
Download validation/imatrix.json from Mike0021/Ling-3.0-tiny-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 1.83 kB
-
https://huggingface.co/Mike0021/Ling-3.0-tiny-GGUF/resolve/main/validation/imatrix.json
- Command line
-
hf download hf://Mike0021/Ling-3.0-tiny-GGUF/validation/imatrix.json
-
curl -L -o imatrix.json https://huggingface.co/Mike0021/Ling-3.0-tiny-GGUF/resolve/main/validation/imatrix.json
1.83 kB
| { | |
| "bytes": 44016768, | |
| "chunk_count": 162, | |
| "chunk_size": 4096, | |
| "datasets": [ | |
| "/workspace/ling3/calibration/bartowski-imatrix-v5-semantic.txt", | |
| "/workspace/ling3/calibration/eaddario/combined_all_micro.txt" | |
| ], | |
| "expert_count_distribution": { | |
| "maximum": 326023.0, | |
| "median": 33514.0, | |
| "minimum": 16.0, | |
| "p01": 687.0, | |
| "p05": 3574.0, | |
| "p95": 103901.0, | |
| "p99": 195189.0 | |
| }, | |
| "expert_count_tensors": 69, | |
| "expert_count_values": 8832, | |
| "failures": [], | |
| "filename": "Ling-3.0-tiny-imatrix.gguf", | |
| "lowest_expert_slots": [ | |
| { | |
| "count": 16.0, | |
| "expert": 23, | |
| "tensor": "blk.21.ffn_down_exps.weight" | |
| }, | |
| { | |
| "count": 16.0, | |
| "expert": 23, | |
| "tensor": "blk.21.ffn_gate_exps.weight" | |
| }, | |
| { | |
| "count": 16.0, | |
| "expert": 23, | |
| "tensor": "blk.21.ffn_up_exps.weight" | |
| }, | |
| { | |
| "count": 27.0, | |
| "expert": 109, | |
| "tensor": "blk.22.ffn_down_exps.weight" | |
| }, | |
| { | |
| "count": 27.0, | |
| "expert": 109, | |
| "tensor": "blk.22.ffn_gate_exps.weight" | |
| }, | |
| { | |
| "count": 27.0, | |
| "expert": 109, | |
| "tensor": "blk.22.ffn_up_exps.weight" | |
| }, | |
| { | |
| "count": 34.0, | |
| "expert": 119, | |
| "tensor": "blk.20.ffn_down_exps.weight" | |
| }, | |
| { | |
| "count": 34.0, | |
| "expert": 119, | |
| "tensor": "blk.20.ffn_gate_exps.weight" | |
| }, | |
| { | |
| "count": 34.0, | |
| "expert": 119, | |
| "tensor": "blk.20.ffn_up_exps.weight" | |
| }, | |
| { | |
| "count": 49.0, | |
| "expert": 90, | |
| "tensor": "blk.23.ffn_down_exps.weight" | |
| } | |
| ], | |
| "matrix_entries": 332, | |
| "nonfinite_count_values": 0, | |
| "nonfinite_sum_values": 0, | |
| "passed": true, | |
| "sha256": "e8b15d131f9ce294f922c5c387f7a69829c12100d6a35bb1635a2b859083c3f0", | |
| "tensor_count": 664, | |
| "tokens_processed": 663552, | |
| "zero_expert_counts": 0 | |
| } | |