Instructions to use shibajustfor/58d597c9-091f-4c62-b174-30cc8ad298b3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shibajustfor/58d597c9-091f-4c62-b174-30cc8ad298b3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("katuni4ka/tiny-random-olmo-hf") model = PeftModel.from_pretrained(base_model, "shibajustfor/58d597c9-091f-4c62-b174-30cc8ad298b3") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from shibajustfor/58d597c9-091f-4c62-b174-30cc8ad298b3: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/shibajustfor/58d597c9-091f-4c62-b174-30cc8ad298b3/resolve/main/training_args.bin
- Command line
-
hf download hf://shibajustfor/58d597c9-091f-4c62-b174-30cc8ad298b3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/shibajustfor/58d597c9-091f-4c62-b174-30cc8ad298b3/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- 400c39bbf8ff9aacea496f972ae7984557fa38bbe41c85bac6fe7c4249134707
- Size of remote file:
- 6.78 kB
- SHA256:
- a17e4fd6b8a045c33cd861b2e70557a52b80ad9bfece747a49dcae16123affc1
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