Instructions to use beddi/try_unsloth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use beddi/try_unsloth with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "beddi/try_unsloth") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from beddi/try_unsloth: direct link, hf CLI and curl.
- Browser
- Download file 5.56 kB
-
https://huggingface.co/beddi/try_unsloth/resolve/main/training_args.bin
- Command line
-
hf download hf://beddi/try_unsloth/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/beddi/try_unsloth/resolve/main/training_args.bin
5.56 kB
- Xet hash:
- e0f57eaefe6abfaa6dfbc51ec745857190ea599eb47fcc8e38bc39942773053e
- Size of remote file:
- 5.56 kB
- SHA256:
- fd858955ca61ba2defef3ad8995112118b2a7663fe0bc31693371ceed333733b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.