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.

onekq-ai/OneSQL-v0.2-Qwen-3B-GGUF

Disclaimer

Introduction

Performances

Quick start

Caveats

onekq-ai/
OneSQL-v0.2-Qwen-3B-GGUF