Instructions to use rreit/gemma-7b-prompts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rreit/gemma-7b-prompts with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-7b") model = PeftModel.from_pretrained(base_model, "rreit/gemma-7b-prompts") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from rreit/gemma-7b-prompts: direct link, hf CLI and curl.
- Browser
- Download file 4.92 kB
-
https://huggingface.co/rreit/gemma-7b-prompts/resolve/main/training_args.bin
- Command line
-
hf download hf://rreit/gemma-7b-prompts/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/rreit/gemma-7b-prompts/resolve/main/training_args.bin
4.92 kB
- Xet hash:
- 38d898daf3e4ce44269063bb1af40ad7aa278442996f4d9c58ebb9963311aff4
- Size of remote file:
- 4.92 kB
- SHA256:
- f6a46a47d89114866199b0ab4c50ae167c24c31675042e25941727b1c32fb57f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.