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
Qwen2.5-Coder-7B โ Text-to-SQL (Spider)
LoRA adapter fine-tuned on the Spider benchmark.
- 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 | Demo: Gradio Space
Usage
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")
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