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/compare_tokenizer.py from Mike0021/Ling-3.0-tiny-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 2.53 kB
-
https://huggingface.co/Mike0021/Ling-3.0-tiny-GGUF/resolve/main/validation/compare_tokenizer.py
- Command line
-
hf download hf://Mike0021/Ling-3.0-tiny-GGUF/validation/compare_tokenizer.py
-
curl -L -o compare_tokenizer.py https://huggingface.co/Mike0021/Ling-3.0-tiny-GGUF/resolve/main/validation/compare_tokenizer.py
2.53 kB
| #!/usr/bin/env python3 | |
| """Compare GGUF token IDs with the pinned Hugging Face tokenizer outputs.""" | |
| from __future__ import annotations | |
| import argparse | |
| import ast | |
| import json | |
| import subprocess | |
| from pathlib import Path | |
| def main() -> None: | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--llama-tokenize", type=Path, required=True) | |
| parser.add_argument("--model", type=Path, required=True) | |
| parser.add_argument("--hf-reference", type=Path, required=True) | |
| parser.add_argument("--output", type=Path, required=True) | |
| args = parser.parse_args() | |
| reference = json.loads(args.hf_reference.read_text(encoding="utf-8")) | |
| cases = reference.get("tokenizer_cases") | |
| if not isinstance(cases, list) or len(cases) != 6: | |
| raise SystemExit("HF reference must contain exactly six tokenizer cases") | |
| if any( | |
| not isinstance(case, dict) | |
| or not isinstance(case.get("text"), str) | |
| or not case["text"] | |
| or not isinstance(case.get("token_ids"), list) | |
| or not case["token_ids"] | |
| for case in cases | |
| ): | |
| raise SystemExit("HF reference contains an empty or malformed tokenizer case") | |
| comparisons = [] | |
| for case in cases: | |
| completed = subprocess.run( | |
| [ | |
| str(args.llama_tokenize), | |
| "--model", | |
| str(args.model), | |
| "--prompt", | |
| case["text"], | |
| "--ids", | |
| "--no-bos", | |
| "--offline", | |
| "--log-disable", | |
| ], | |
| check=True, | |
| text=True, | |
| capture_output=True, | |
| ) | |
| gguf_ids = ast.literal_eval(completed.stdout.strip()) | |
| match = gguf_ids == case["token_ids"] | |
| comparisons.append( | |
| { | |
| "text": case["text"], | |
| "hf_token_ids": case["token_ids"], | |
| "gguf_token_ids": gguf_ids, | |
| "exact_match": match, | |
| } | |
| ) | |
| result = { | |
| "cases": comparisons, | |
| "case_count": len(comparisons), | |
| "all_exact": all(case["exact_match"] for case in comparisons), | |
| } | |
| args.output.parent.mkdir(parents=True, exist_ok=True) | |
| args.output.write_text( | |
| json.dumps(result, ensure_ascii=False, indent=2, sort_keys=True) + "\n", | |
| encoding="utf-8", | |
| ) | |
| print(json.dumps(result, ensure_ascii=False, sort_keys=True)) | |
| if not result["all_exact"]: | |
| raise SystemExit("tokenizer mismatch") | |
| if __name__ == "__main__": | |
| main() | |