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