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"
File size: 2,528 Bytes
31596cd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | #!/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()
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