Update README.md

README.md

@@ -1,3 +1,73 @@
 ---
 license: mit
+datasets:
+- gretelai/synthetic_text_to_sql
+language:
+- en
+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
+```
+
+## Inference
+
+```python
+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)
+```