File size: 1,340 Bytes
3485f2f fbd1e85 01c7c85 fbd1e85 01c7c85 fbd1e85 01c7c85 fbd1e85 01c7c85 fbd1e85 01c7c85 fbd1e85 01c7c85 fbd1e85 01c7c85 fbd1e85 01c7c85 fbd1e85 01c7c85 fbd1e85 01c7c85 fbd1e85 01c7c85 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 |
---
license: apache-2.0
base_model: codellama/CodeLlama-7b-Instruct-hf
tags:
- text-to-sql
- spider-dataset
- sqlifyai
- code-generation
library_name: transformers
pipeline_tag: text-generation
---
# SQLifyAI - Text-to-SQL Model
This model was fine-tuned using SQLifyAI on the Spider dataset for converting natural language questions to SQL queries.
## Model Details
- **Base Model**: codellama/CodeLlama-7b-Instruct-hf
- **Dataset**: Spider
- **Training**: Multi-stage curriculum learning with advanced schema linking
- **Commit**: 30-minute rapid test training run
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("dattheshshenoy/sqlifyai-30min-test")
model = AutoModelForCausalLM.from_pretrained("dattheshshenoy/sqlifyai-30min-test")
# Generate SQL
question = "What are the names of all students?"
schema = "CREATE TABLE students (id INT, name VARCHAR(50));"
prompt = f"### Question: {question}\n### Schema: {schema}\n### SQL:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
sql = tokenizer.decode(outputs[0], skip_special_tokens=True).split("### SQL:")[-1].strip()
```
## Performance
- Trained with advanced schema linking and curriculum learning
- Optimized for Spider dataset evaluation metrics
|