|
|
| --- |
| language: en |
| license: apache-2.0 |
| tags: |
| - mistral |
| - sql |
| - lora |
| - instruction-tuning |
| datasets: |
| - custom_sql_dataset |
| --- |
| |
| # SQL Query Generation Model |
|
|
| This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.3 specialized for SQL query generation. |
|
|
| ## Model Details |
|
|
| - **Base Model**: mistralai/Mistral-7B-Instruct-v0.3 |
| - **Training Method**: LoRA (Rank=16, Alpha=32) |
| - **Task**: SQL query generation from natural language instructions |
| - **Training Framework**: Unsloth |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| |
| # Load the model |
| tokenizer = AutoTokenizer.from_pretrained("exaler/aaa-2-sql-2") |
| model = AutoModelForCausalLM.from_pretrained("exaler/aaa-2-sql-2") |
| |
| # Format your prompt |
| instruction = """You are an expert SQL query generator. Database schema: |
| Table: [dbo].[Users] |
| Columns: [ID], [Name], [Email], [CreatedDate] |
| Table: [dbo].[Orders] |
| Columns: [OrderID], [UserID], [Amount], [Status], [OrderDate] |
| """ |
| |
| input_text = "Find all users who placed orders with amount greater than 1000" |
| |
| prompt = f"<s>[INST] {instruction}\n\n{input_text} [/INST]" |
| |
| # Generate SQL |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate(inputs=inputs.input_ids, max_new_tokens=512, temperature=0.0) |
| response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) |
| print(response) |
| ``` |
|
|
| ## Training Dataset |
|
|
| The model was trained on a custom dataset of SQL queries with their corresponding natural language descriptions. |
|
|
| ## Limitations |
|
|
| - The model is optimized for the specific SQL database schema it was trained on |
| - Performance may vary for database schemas significantly different from the training data |
|
|
