File size: 2,890 Bytes
1203000 4421773 e0f70bc 4421773 1203000 4421773 2f45f54 4421773 55a3202 4421773 55a3202 4421773 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 |
---
license: mit
datasets:
- gretelai/synthetic_text_to_sql
language:
- en
inference:
parameters:
do_sample: False
max_new_tokens: 250
temperature: 0.7
library_name: transformers
pipeline_tag: text2text-generation
---
# Gemma 2B Fine-Tuned SQL Generator
## Introduction
The Gemma 2B SQL Generator is a specialized version of the Gemma 2B model, fine-tuned to generate SQL queries based on a given SQL context. This model has been tailored to assist developers and analysts in generating accurate SQL queries automatically, enhancing productivity and reducing the scope for errors.
## Model Details
- **Model Type:** Gemma 2B
- **Fine-Tuning Details:** The model was fine-tuned specifically for generating SQL queries.
- **Training Loss:** Achieved a training loss of 0.3, indicating a high level of accuracy in SQL query generation.
## Installation
To set up the necessary environment for using the SQL Generator, run the following commands:
```bash
pip install torch torch
pip install transformers
```
## how to Fine Tune
here is the github link [click here](https://github.com/theSuriya/Gemma-SQL-Generator/tree/main)
## Inference
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("suriya7/Gemma2B-Finetuned-Sql-Generator")
model = AutoModelForCausalLM.from_pretrained("suriya7/Gemma2B-Finetuned-Sql-Generator")
prompt_template = """
<start_of_turn>user
You are an intelligent AI specialized in generating SQL queries.
Your task is to assist users in formulating SQL queries to retrieve specific information from a database.
Please provide the SQL query corresponding to the given prompt and context:
Prompt:
find the price of laptop
Context:
CREATE TABLE products (
product_id INT,
product_name VARCHAR(100),
category VARCHAR(50),
price DECIMAL(10, 2),
stock_quantity INT
);
INSERT INTO products (product_id, product_name, category, price, stock_quantity)
VALUES
(1, 'Smartphone', 'Electronics', 599.99, 100),
(2, 'Laptop', 'Electronics', 999.99, 50),
(3, 'Headphones', 'Electronics', 99.99, 200),
(4, 'T-shirt', 'Apparel', 19.99, 300),
(5, 'Jeans', 'Apparel', 49.99, 150);<end_of_turn>
<start_of_turn>model
"""
prompt = prompt_template
encodeds = tokenizer(prompt, return_tensors="pt", add_special_tokens=True).input_ids
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
inputs = encodeds.to(device)
# Increase max_new_tokens if needed
generated_ids = model.generate(inputs, max_new_tokens=1000, do_sample=True, temperature = 0.7,pad_token_id=tokenizer.eos_token_id)
ans = ''
for i in tokenizer.decode(generated_ids[0], skip_special_tokens=True).split('<end_of_turn>')[:2]:
ans += i
# Extract only the model's answer
model_answer = ans.split("model")[1].strip()
print(model_answer)
``` |