Instructions to use onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF", dtype="auto")

llama-cpp-python

How to use onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF",
	filename="OneSQL-v0.2-Qwen-3B-Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use onekq-ai/OneSQL-v0.2-Qwen-3B-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 onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf onekq-ai/OneSQL-v0.2-Qwen-3B-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 onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf onekq-ai/OneSQL-v0.2-Qwen-3B-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-v0.2-Qwen-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf onekq-ai/OneSQL-v0.2-Qwen-3B-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-v0.2-Qwen-3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF with Ollama:
```
ollama run hf.co/onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use onekq-ai/OneSQL-v0.2-Qwen-3B-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-v0.2-Qwen-3B-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-v0.2-Qwen-3B-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-v0.2-Qwen-3B-GGUF to start chatting

How to use onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf onekq-ai/OneSQL-v0.2-Qwen-3B-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-v0.2-Qwen-3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use onekq-ai/OneSQL-v0.2-Qwen-3B-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 onekq-ai/OneSQL-v0.2-Qwen-3B-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-v0.2-Qwen-3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf onekq-ai/OneSQL-v0.2-Qwen-3B-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 "onekq-ai/OneSQL-v0.2-Qwen-3B-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"

Docker Model Runner
How to use onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF with Docker Model Runner:
```
docker model run hf.co/onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF:Q4_K_M
```

Lemonade

How to use onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OneSQL-v0.2-Qwen-3B-GGUF-Q4_K_M

List all available models

lemonade list

Disclaimer

Your email will be used for anonymous survey. It will NOT be shared with anyone.

Introduction

This model is the GGUF version of OneSQL-v0.2-Qwen-3B.

Performances

Below is the self-evaluation results for each quantization and its improvement over OneSQL-v0.1-Qwen-3B-GGUF.

Quantization	EX score	v0.1 EX score
Q4_0	29.59	16.83
Q4_1	32.35	21.85
Q4_K_S	31.16	22.49
Q4_K_M	31.03	21.85
Q5_0	31.24	23.40
Q5_1	33.27	23.53
Q5_K_S	34.38	22.77
Q5_K_M	34.49	23.73
Q6_K	32.68	24.51
Q8_0	32.59	24.90

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 "

PROMPT=$(printf "<|im_start|>system\nYou are a SQL expert. Return code only.<|im_end|>\n<|im_start|>user\n%s<|im_end|>\n<|im_start|>assistant\n" "$PROMPT")

llama.cpp/build/bin/llama-run file://OneSQL-v0.2-Qwen-3B-Q4_K_M.gguf "$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 2-bit and 3-bit quantizations suffer from repetitive and unrelevant output token, hence are not recommended for usage.