--- 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"[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