Instructions to use minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned 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 minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned: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 minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M

Use Docker

docker model run hf.co/minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M

Ollama
How to use minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned with Ollama:
```
ollama run hf.co/minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M
```
Unsloth Desktop

How to use minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned: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": "minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned with Docker Model Runner:
```
docker model run hf.co/minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M
```

Lemonade

How to use minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-3B-Instruct-Text2SQL-Tuned-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned: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 minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned: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 "minktn/Qwen2.5-3B-Instruct-Text2SQL-Tuned: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"

Qwen2.5-3B-Instruct-Text2SQL-Tuned

File size: 1,040 Bytes

---
license: apache-2.0
base_model: Qwen/Qwen2.5-3B-Instruct
tags:
  - text-to-sql
  - sql
  - qwen2.5
  - gguf
  - llama-cpp
language:
  - en
pipeline_tag: text-generation
---

# Qwen2.5-3B-Instruct-Text2SQL-Tuned

This model is a specialized Text-to-SQL adaptation inheriting directly from the base architecture [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct). It is fine-tuned to translate complex natural language queries into executable, standardized SQLite syntax given relational schema definitions (DDL).

---

## 📌 Heritage & Model Overview

- **Base Architecture**: `Qwen/Qwen2.5-3B-Instruct`
- **Domain Specialization**: Text-to-SQL (Spider-style multi-domain benchmarks)
- **Primary Task**: Schema-grounded query generation
- **Available Artifacts**:
  - `final_model/`: Full-precision PyTorch / SafeTensors weights & tokenizers (for evaluation & fine-tuning pipelines).
  - `qwen2.5-3b-instruct.Q4_K_M.gguf`: 4-bit medium quantized artifact optimized for lightweight edge and local CPU inference.