Instructions to use hotaru23/mathgenieFinetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hotaru23/mathgenieFinetune with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("MathGenie/MathGenie-InterLM-20B") model = PeftModel.from_pretrained(base_model, "hotaru23/mathgenieFinetune") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from hotaru23/mathgenieFinetune: direct link, hf CLI and curl.
- Browser
- Download file 10.5 MB
-
https://huggingface.co/hotaru23/mathgenieFinetune/resolve/main/tokenizer.json
- Command line
-
hf download hf://hotaru23/mathgenieFinetune/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/hotaru23/mathgenieFinetune/resolve/main/tokenizer.json
10.5 MB
- Xet hash:
- f09232822f5f0ad5851eaa854fb224ef6c6f8e05e568dd3c96303f0d403680b0
- Size of remote file:
- 10.5 MB
- SHA256:
- 1d93651051edc9e95c11c8ad6407ec47db96a32180d5cd5dc4748d1ea892e652
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.