Instructions to use Jatme26/conv-sum-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Jatme26/conv-sum-test with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("decapoda-research/llama-7b-hf") model = PeftModel.from_pretrained(base_model, "Jatme26/conv-sum-test") - Notebooks
- Google Colab
- Kaggle
Download optimizer.pt from Jatme26/conv-sum-test: direct link, hf CLI and curl.
- Browser
- Download file 33.7 MB
-
https://huggingface.co/Jatme26/conv-sum-test/resolve/main/optimizer.pt
- Command line
-
hf download hf://Jatme26/conv-sum-test/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Jatme26/conv-sum-test/resolve/main/optimizer.pt
33.7 MB
- Xet hash:
- 002e661947726e1978cb82b5afc63a8465be6ecb0b60ae7cbd544329a0bfd3c6
- Size of remote file:
- 33.7 MB
- SHA256:
- 13ff9d7bd979f68a4fe58702525d04c0c16f3e9b44b02216fbf2102f130cffe1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.