Instructions to use mehmet1899/llama32-3b-instruct-nl2sql-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mehmet1899/llama32-3b-instruct-nl2sql-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-3B-Instruct") model = PeftModel.from_pretrained(base_model, "mehmet1899/llama32-3b-instruct-nl2sql-lora") - Notebooks
- Google Colab
- Kaggle
| 8c7c14a41632a1ab3564fa25521f8737237795b5dcae7730abfe0ab4aca88480 adapter_config.json | |
| fcd4241f7a2e8e0388f13f0dd9517486cbee43fc3169c983a54e7b716c0e502d adapter_model.safetensors | |
| bcfbdd3b4b74206c9a61aff857b91e32cfe6f54dd329eb134eccf566b2be8c41 README.md | |
| 5816fce10444e03c2e9ee1ef8a4a1ea61ae7e69e438613f3b17b69d0426223a4 chat_template.jinja | |
| 58d954db45152000b3596db19f22648213abd8a65893610380e527b7c87802b6 tokenizer_config.json | |
| 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b tokenizer.json | |
| f5365d265fe999047a78dc0ebb94d8deb563eabee0ccdadc4de149f280ceb7ea training_metadata.json | |
| 5947a547d51049836504823fbc88b1b621376614e798654b0bf82240d3373f69 training_history.csv | |
| 2e40d8195699ea3b1a158430527f4bbd8468c922c004670d94f31cfc4e77c71b training_history.jsonl | |