Instructions to use kvjv/sixthmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kvjv/sixthmodel 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/sixthmodel") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from kvjv/sixthmodel: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/kvjv/sixthmodel/resolve/main/training_args.bin
- Command line
-
hf download hf://kvjv/sixthmodel/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/kvjv/sixthmodel/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- 99ab088025ebeb7328968b342f17a7635c3ddb3fbbd3ef67c00c8ab96fe02e3b
- Size of remote file:
- 5.11 kB
- SHA256:
- 3af1d65b768e371d3fd345d5af4097275a6f564701c829c22d259a78f319798c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.