| | --- |
| | license: apache-2.0 |
| | language: |
| | - en |
| | base_model: |
| | - Qwen/Qwen2.5-Coder-3B-Instruct |
| | tags: |
| | - text-to-sql |
| | - fine-tuned |
| | - qwen |
| | pipeline_tag: text-generation |
| | --- |
| | This is a fine-tuned version of Qwen/Qwen2.5-Coder-3B-Instruct for generating SQL queries from natural language questions. The model was fine-tuned using LoRA (r=16) on a subset of the Spider dataset and merged into a standalone model, eliminating the need for the peft library during inference. |
| | Usage |
| | To use the model for SQL query generation: |
| | from transformers import AutoModelForCausalLM, AutoTokenizer |
| | import torch |
| |
|
| | # Load model and tokenizer |
| | model_name = "Piyush026/Qwen2.5-Coder-3B-sql-finetuned" # Replace with your repo ID |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | model = AutoModelForCausalLM.from_pretrained( |
| | model_name, |
| | torch_dtype=torch.float16, |
| | device_map="auto", |
| | trust_remote_code=True |
| | ) |
| | |
| |
|
| | # Generate SQL query Example |
| | prompt = """ |
| | Database: university |
| | Schema: |
| | - students: [student_id, first_name, last_name, department_code, gpa, major] |
| | - departments: [department_code, department_name] |
| | - courses: [course_number, course_title, professor_id] |
| | - instructors: [professor_id, last_name] |
| | Question: List all students. |
| | """ |
| | inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
| | outputs = model.generate(**inputs, max_length=200) |
| | print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| | |
| | |
| | ## Training Details |
| | |
| | Base Model: Qwen/Qwen2.5-Coder-3B-Instruct |
| | Fine-Tuning: LoRA (r=16, lora_alpha=32, lora_dropout=0.05) on a 1000-sample subset of the Spider dataset. |
| | Environment: Lightning AI Studio with Tesla T4 GPU. |
| | Merged Model: The LoRA adapters were merged into the base model using merge_and_unload for standalone inference. |
