Instructions to use Gansaw98/qwen2.5-coder-7b-text2sql-spider with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Gansaw98/qwen2.5-coder-7b-text2sql-spider with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") model = PeftModel.from_pretrained(base_model, "Gansaw98/qwen2.5-coder-7b-text2sql-spider") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen2.5-Coder-7B-Instruct | |
| tags: | |
| - text-to-sql | |
| - lora | |
| - qlora | |
| - spider | |
| - peft | |
| # Qwen2.5-Coder-7B — Text-to-SQL (Spider) | |
| LoRA adapter fine-tuned on the [Spider benchmark](https://yale-lily.github.io/spider). | |
| - **77.7% execution accuracy** on Spider dev set (official Yale-LILY evaluation) | |
| - **+24.5% over zero-shot Llama 3.3-70B** baseline | |
| - Training: 4-bit QLoRA, r=16, 2 epochs, Kaggle T4 GPU | |
| **GitHub:** [text-to-sql-spider](https://github.com/Gansaw98/text-to-sql-spider) | **Demo:** [Gradio Space](https://huggingface.co/spaces/Gansaw98/text-to-sql-demo) | |
| ## Usage | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from peft import PeftModel | |
| import torch | |
| tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct") | |
| base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct", torch_dtype=torch.float16) | |
| model = PeftModel.from_pretrained(base, "Gansaw98/qwen2.5-coder-7b-text2sql-spider") |