Instructions to use Shibyan/llama3.1_Odata2SQL-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Shibyan/llama3.1_Odata2SQL-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "Shibyan/llama3.1_Odata2SQL-lora") - Notebooks
- Google Colab
- Kaggle
Download checkpoint-30/training_args.bin from Shibyan/llama3.1_Odata2SQL-lora: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/Shibyan/llama3.1_Odata2SQL-lora/resolve/main/checkpoint-30/training_args.bin
- Command line
-
hf download hf://Shibyan/llama3.1_Odata2SQL-lora/checkpoint-30/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Shibyan/llama3.1_Odata2SQL-lora/resolve/main/checkpoint-30/training_args.bin
5.37 kB
- Xet hash:
- 243461bcbd20463e1eff69fce44211614b5746afd217e3c9c4a9e347277543cd
- Size of remote file:
- 5.37 kB
- SHA256:
- bdc7c75dc36fde857d634bf6a29efbd26c628f3085439f9644feae59f731478e
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