Instructions to use daniel40/22d83212-a107-4136-b5a9-69e75450331c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use daniel40/22d83212-a107-4136-b5a9-69e75450331c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.1-Storm-8B") model = PeftModel.from_pretrained(base_model, "daniel40/22d83212-a107-4136-b5a9-69e75450331c") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from daniel40/22d83212-a107-4136-b5a9-69e75450331c: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/daniel40/22d83212-a107-4136-b5a9-69e75450331c/resolve/main/training_args.bin
- Command line
-
hf download hf://daniel40/22d83212-a107-4136-b5a9-69e75450331c/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/daniel40/22d83212-a107-4136-b5a9-69e75450331c/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- a0ad9a4d74a5b17d818a4047374460252de70775bf8d8753fd7781773f276835
- Size of remote file:
- 6.78 kB
- SHA256:
- 7d311af1f432638ae2405eba92206bcc19aa608e8e2053386051abbe8ac160c7
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