Instructions to use kenilp7/gemma-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kenilp7/gemma-sql with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-27b-it") model = PeftModel.from_pretrained(base_model, "kenilp7/gemma-sql") - Notebooks
- Google Colab
- Kaggle
| base_model: google/gemma-3-27b-it | |
| library_name: peft | |
| ## Gemma-3-27B Finetuned on Text-to-SQL | |
| This model is a LoRA-finetuned version of `google/gemma-3-27b-it` for generating MySQL queries. | |
| ## Intended Use | |
| For research on Text-to-SQL generation tasks. | |
| ## Training | |
| Fine-tuned on BIRD Train Data consisiting of around 9,500 examples. | |
| ## How to use | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model = AutoModelForCausalLM.from_pretrained("kenilp7/gemma-sql") | |
| tokenizer = AutoTokenizer.from_pretrained("kenilp7/gemma-sql") | |
| ``` | |
| ## Model Description | |
| - **Language(s) (NLP):** SQL Generation | |
| - **Finetuned from model [optional]:** google/gemma-3-27b-it | |