Instructions to use richlai/llama381binstruct_summarize_short with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use richlai/llama381binstruct_summarize_short with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "richlai/llama381binstruct_summarize_short") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from richlai/llama381binstruct_summarize_short: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/richlai/llama381binstruct_summarize_short/resolve/main/training_args.bin
- Command line
-
hf download hf://richlai/llama381binstruct_summarize_short/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/richlai/llama381binstruct_summarize_short/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- f2394d5df9374a9108eaf3d87987b5730578097760dad0f1bc63185074a5dfb6
- Size of remote file:
- 5.5 kB
- SHA256:
- 18ac6993230b61e8b01f3b2217db4a084d31114398c9fcf217ef16a015b8ba5f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.