Instructions to use onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF", dtype="auto")

llama-cpp-python

How to use onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF",
	filename="OneSQL-v0.1-Qwen-7B-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf onekq-ai/OneSQL-v0.1-Qwen-7B-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 onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf onekq-ai/OneSQL-v0.1-Qwen-7B-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 onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF with Ollama:
```
ollama run hf.co/onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF to start chatting

Pi new

How to use onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf onekq-ai/OneSQL-v0.1-Qwen-7B-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 onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M
```

Lemonade

How to use onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull onekq-ai/OneSQL-v0.1-Qwen-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OneSQL-v0.1-Qwen-7B-GGUF-Q4_K_M

List all available models

lemonade list

Introduction

This model is the GGUF version of OneSQL-v0.1-Qwen-7B. You can also find it on Ollama.

Performances

The self-evaluation EX score of the original model is 56.19 (compared to 63.33 by the 32B model on the BIRD leaderboard. Below is the self-evaluation results for each quantization.

Quantization	EX score
Q2_K	29.79
Q3_K_S	36.31
Q3_K_M	39.24
Q3_K_L	40.14
Q4_1	39.06
Q4_K_S	42.69
Q4_K_M	43.95
Q5_0	43.84
Q5_1	41.00
Q5_K_S	42.20
Q5_K_M	42.07
Q6_K	41.68
Q8_0	41.09

Quick start

To use this model, craft your prompt to start with your database schema in the form of CREATE TABLE, followed by your natural language query preceded by --. Make sure your prompt ends with SELECT in order for the model to finish the query for you. There is no need to set other parameters like temperature or max token limit.

PROMPT="CREATE TABLE students (
    id INTEGER PRIMARY KEY,
    name TEXT,
    age INTEGER,
    grade TEXT
);

-- Find the three youngest students
SELECT "

ollama run onekq-ai/OneSQL-v0.1-Qwen:7B-Q4_K_M "$PROMPT"

The model response is the finished SQL query without SELECT

* FROM students ORDER BY age ASC LIMIT 3

Caveats

The performance drop from the original model is due to quantization itself, and the lack of beam search support in llama.cpp framework. Use at your own discretion.
The Q4_0 quantization suffers from repetitive output token, hence is not recommended for usage.