Instructions to use liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use liodon-ai/Qwen2.5-Coder-3B-Instruct-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 liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/Qwen2.5-Coder-3B-Instruct-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 liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "liodon-ai/Qwen2.5-Coder-3B-Instruct-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": "liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M

Ollama
How to use liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf liodon-ai/Qwen2.5-Coder-3B-Instruct-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": "liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Coder-3B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use liodon-ai/Qwen2.5-Coder-3B-Instruct-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 liodon-ai/Qwen2.5-Coder-3B-Instruct-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 liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf liodon-ai/Qwen2.5-Coder-3B-Instruct-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 "liodon-ai/Qwen2.5-Coder-3B-Instruct-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"

Qwen2.5-Coder-3B-Instruct — GGUF

GGUF quantizations of Qwen/Qwen2.5-Coder-3B-Instruct, published by Liodon AI.

Quick Start

llama.cpp

llama-cli -hf liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M

Ollama

ollama run hf.co/liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF:Q4_K_M

LM Studio / Jan — search liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF and pick your quant.

Quants

Quant	Size	VRAM est.	Notes
`Q2_K`	1.27 GB	~1 GB	2-bit, smallest standard
`Q3_K_M`	1.59 GB	~2 GB	3-bit, good for 8 GB VRAM
`Q4_0`	1.82 GB	~2 GB	4-bit original
`Q4_K_M`	1.93 GB	~2 GB	4-bit (recommended sweet spot)
`Q5_K_M`	2.22 GB	~3 GB	5-bit, high quality
`Q6_K`	2.54 GB	~3 GB	6-bit, near-lossless
`Q8_0`	3.29 GB	~4 GB	8-bit, essentially lossless

For higher-quality sub-4-bit quants with iMatrix calibration, see liodon-ai/Qwen2.5-Coder-3B-Instruct-imatrix-GGUF

Source

Model: Qwen/Qwen2.5-Coder-3B-Instruct
License: other

Citation

@misc{liodonai_qwen2_5_coder_3b_instruct_gguf,
  title        = {Qwen2.5-Coder-3B-Instruct — GGUF},
  author       = {{Liodon AI}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF}},
  note         = {GGUF quantization of Qwen/Qwen2.5-Coder-3B-Instruct}
}

Quantized by Liodon AI

Downloads last month: -

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for liodon-ai/Qwen2.5-Coder-3B-Instruct-GGUF

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-Coder-3B

Finetuned

Qwen/Qwen2.5-Coder-3B-Instruct

Quantized

(146)

this model