onekq-ai/OneSQL-v0.2-Qwen-7B-GGUF

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-7B.

Performances

Below is the self-evaluation results for each quantization and its improvement over OneSQL-v0.1-Qwen-7B-GGUF.

Quantization	EX score	v0.1 EX score
Q2_K	39.60	29.79
Q3_K_S	38.79	36.31
Q3_K_M	41.93	39.24
Q3_K_L	45.49	40.14
Q4_1	39.06
Q4_K_S	42.69
Q4_K_M	44.19	43.95
Q5_0	43.63	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 "

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-7B-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.