Instructions to use KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use KRadim/Qwen2.5-Coder-3B-text2cypher-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 KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KRadim/Qwen2.5-Coder-3B-text2cypher-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 KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KRadim/Qwen2.5-Coder-3B-text2cypher-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 KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KRadim/Qwen2.5-Coder-3B-text2cypher-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 KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M

Use Docker

docker model run hf.co/KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KRadim/Qwen2.5-Coder-3B-text2cypher-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": "KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M

Ollama
How to use KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF with Ollama:
```
ollama run hf.co/KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M
```
Unsloth Desktop

How to use KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KRadim/Qwen2.5-Coder-3B-text2cypher-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": "KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF with Docker Model Runner:
```
docker model run hf.co/KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M
```

Lemonade

How to use KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-Coder-3B-text2cypher-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use KRadim/Qwen2.5-Coder-3B-text2cypher-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 KRadim/Qwen2.5-Coder-3B-text2cypher-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 KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KRadim/Qwen2.5-Coder-3B-text2cypher-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 "KRadim/Qwen2.5-Coder-3B-text2cypher-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 Text-to-Cypher (Neo4j) - Run #2

This model is a fine-tuned version of unsloth/Qwen2.5-Coder-3B-Instruct optimized for translating natural language questions into accurate Neo4j Cypher queries using a structured database schema.

Number of samples used: 70000

Model Details

Base Architecture: Qwen2.5-Coder-3B-Instruct
Fine-Tuning Method: QLoRA (Rank 16, Alpha 16)
Dataset Used: KRadim/multilingual-text2cypher-2025 (35000 examples)
Epochs: 1
Frameworks: Unsloth, Hugging Face TRL, PEFT, PyTorch

Prompts & Schema Usage

The model expects system prompts containing the Neo4j schema structure:

<|im_start|>system
You are an expert in Neo4j databases and the Cypher query language. Your task is to convert natural language queries into accurate and efficient Cypher queries based on the provided database schema.

### Database Schema:
[PROVIDE DATABASE SCHEMA HERE]<|im_end|>
<|im_start|>user
[YOUR NATURAL LANGUAGE QUESTION]<|im_end|>
<|im_start|>assistant

Running with Ollama (GGUF)

You can run the exported GGUF model (KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF) directly in Ollama:

ollama run hf.co/KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF:Q4_K_M

Downloads last month: 63

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

KRadim
/

Qwen2.5-Coder-3B-text2cypher-GGUF

Qwen2.5-Coder-3B Text-to-Cypher (Neo4j) - Run #2

Model Details

Prompts & Schema Usage

Running with Ollama (GGUF)

Dataset used to train KRadim/Qwen2.5-Coder-3B-text2cypher-GGUF