Instructions to use likhith231/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use likhith231/results with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf") model = PeftModel.from_pretrained(base_model, "likhith231/results") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from likhith231/results: direct link, hf CLI and curl.
- Browser
- Download file 4.66 kB
-
https://huggingface.co/likhith231/results/resolve/main/training_args.bin
- Command line
-
hf download hf://likhith231/results/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/likhith231/results/resolve/main/training_args.bin
4.66 kB
- Xet hash:
- 223597e8de79da1ff925a4c6cc8a6ebb7a2465f4ef0c05fe3def4928adfaf3f3
- Size of remote file:
- 4.66 kB
- SHA256:
- 1d5bf477a9d4456e0433f1d9cb743f67d8d6ca85461cc6ab3ad044c0c228d75b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.