Instructions to use Liu-Xiang/sql-code-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Liu-Xiang/sql-code-llama with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-hf") model = PeftModel.from_pretrained(base_model, "Liu-Xiang/sql-code-llama") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Liu-Xiang/sql-code-llama: direct link, hf CLI and curl.
- Browser
- Download file 4.28 kB
-
https://huggingface.co/Liu-Xiang/sql-code-llama/resolve/8bb02fef1a1fcd0bb73e45034fc202fc76933ea4/training_args.bin
- Command line
-
hf download hf://Liu-Xiang/sql-code-llama@8bb02fef1a1fcd0bb73e45034fc202fc76933ea4/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Liu-Xiang/sql-code-llama/resolve/8bb02fef1a1fcd0bb73e45034fc202fc76933ea4/training_args.bin
4.28 kB
- Xet hash:
- ff586c080a4005d8a449e73a90591d0489b4cc82e14c8a63ebc0a5cc53f1ac70
- Size of remote file:
- 4.28 kB
- SHA256:
- e5b89f5b582d03f6386acb0740f84fa158a6734f7ec58680c84cc7b86fac274a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.