Instructions to use dimasik87/72c2c260-2713-4915-922b-1925fe806e3d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik87/72c2c260-2713-4915-922b-1925fe806e3d with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0") model = PeftModel.from_pretrained(base_model, "dimasik87/72c2c260-2713-4915-922b-1925fe806e3d") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from dimasik87/72c2c260-2713-4915-922b-1925fe806e3d: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/dimasik87/72c2c260-2713-4915-922b-1925fe806e3d/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://dimasik87/72c2c260-2713-4915-922b-1925fe806e3d/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/dimasik87/72c2c260-2713-4915-922b-1925fe806e3d/resolve/main/last-checkpoint/optimizer.pt
336 MB
- Xet hash:
- 6eadd92dc0219c00eff00644daca38168c994a49d21c3c22470062346eff68e7
- Size of remote file:
- 336 MB
- SHA256:
- c93347fbdb52ec5c1b8e7207dacd486b8dd5ad5716a4a4ac0d23d4112e09c57e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.