Instructions to use sachiin051/Text2SQL-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sachiin051/Text2SQL-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B") model = PeftModel.from_pretrained(base_model, "sachiin051/Text2SQL-LoRA") - Notebooks
- Google Colab
- Kaggle
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Download README.md from sachiin051/Text2SQL-LoRA: direct link, hf CLI and curl.
- Browser
- Download file 554 Bytes
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https://huggingface.co/sachiin051/Text2SQL-LoRA/resolve/main/README.md
- Command line
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hf download hf://sachiin051/Text2SQL-LoRA/README.md
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curl -L -o README.md https://huggingface.co/sachiin051/Text2SQL-LoRA/resolve/main/README.md
554 Bytes
| library_name: peft | |
| base_model: Qwen/Qwen2.5-Coder-1.5B | |
| pipeline_tag: text-generation | |
| tags: | |
| - text-to-sql | |
| - sql | |
| - lora | |
| - qwen2.5-coder | |
| - fine-tuned | |
| # Text2SQL-LoRA | |
| A parameter-efficient fine-tuned language model for natural-language-to-SQL generation. | |
| ## Base Model | |
| Qwen/Qwen2.5-Coder-1.5B | |
| ## Fine-tuning | |
| - Method: LoRA | |
| - Rank: 16 | |
| - Alpha: 32 | |
| - Dropout: 0.05 | |
| - Task: Causal Language Modeling | |
| ## Intended Use | |
| Research and experimentation in Text-to-SQL generation. | |
| ## Limitations | |
| Generated SQL should be validated before execution. | |