Instructions to use kvjv/secondmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kvjv/secondmodel with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/paligemma-3b-pt-224") model = PeftModel.from_pretrained(base_model, "kvjv/secondmodel") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kvjv/secondmodel: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/kvjv/secondmodel/resolve/main/training_args.bin
- Command line
-
hf download hf://kvjv/secondmodel/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kvjv/secondmodel/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 05c8c8d6cb30be254630039ac803fec1f3c38dbbcb467924cbe4020308aec4e0
- Size of remote file:
- 5.11 kB
- SHA256:
- 9cabb329217dde0ee0867259f6f972dd700af35c1e6d37f07400313537397f0b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.