Instructions to use cwaud/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cwaud/test with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-1.5B-Instruct") model = PeftModel.from_pretrained(base_model, "cwaud/test") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from cwaud/test: direct link, hf CLI and curl.
- Browser
- Download file 296 MB
-
https://huggingface.co/cwaud/test/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://cwaud/test/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/cwaud/test/resolve/main/last-checkpoint/optimizer.pt
296 MB
- Xet hash:
- 7afbc005dc46825daa15c2e58654582c9954baf065d160a586f115569f1df752
- Size of remote file:
- 296 MB
- SHA256:
- f2215d87d2c96e6f4cafe20a820bcbe9c87c9d11d3f34e4e654c748c5acaf2a2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.