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--- |
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license: apache-2.0 |
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base_model: codellama/CodeLlama-7b-Instruct-hf |
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tags: |
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- text-to-sql |
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- spider-dataset |
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- sqlifyai |
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- code-generation |
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library_name: transformers |
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pipeline_tag: text-generation |
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--- |
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# SQLifyAI - Text-to-SQL Model |
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This model was fine-tuned using SQLifyAI on the Spider dataset for converting natural language questions to SQL queries. |
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## Model Details |
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- **Base Model**: codellama/CodeLlama-7b-Instruct-hf |
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- **Dataset**: Spider |
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- **Training**: Multi-stage curriculum learning with advanced schema linking |
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- **Commit**: 30-minute rapid test training run |
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## Usage |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("dattheshshenoy/sqlifyai-30min-test") |
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model = AutoModelForCausalLM.from_pretrained("dattheshshenoy/sqlifyai-30min-test") |
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# Generate SQL |
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question = "What are the names of all students?" |
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schema = "CREATE TABLE students (id INT, name VARCHAR(50));" |
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prompt = f"### Question: {question}\n### Schema: {schema}\n### SQL:" |
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inputs = tokenizer(prompt, return_tensors="pt") |
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outputs = model.generate(**inputs, max_new_tokens=128) |
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sql = tokenizer.decode(outputs[0], skip_special_tokens=True).split("### SQL:")[-1].strip() |
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``` |
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## Performance |
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- Trained with advanced schema linking and curriculum learning |
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- Optimized for Spider dataset evaluation metrics |
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