Instructions to use adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("fxmarty/tiny-random-GemmaForCausalLM") model = PeftModel.from_pretrained(base_model, "adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7/resolve/main/training_args.bin
- Command line
-
hf download hf://adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/adammandic87/532b01b2-d434-48fd-a7b8-d21791366bd7/resolve/main/training_args.bin
6.78 kB
- Xet hash:
- e2086268915e53d3b7917354a1732140345732d3d8cfa4f45edc27f18b5dc465
- Size of remote file:
- 6.78 kB
- SHA256:
- 5d511bb3a31420ab28a13489e7e63798f65321376d09c41a393276779649bf2c
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