Instructions to use vdos/49adb33f-c94b-4760-b90b-7502e63f9910 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use vdos/49adb33f-c94b-4760-b90b-7502e63f9910 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, "vdos/49adb33f-c94b-4760-b90b-7502e63f9910") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from vdos/49adb33f-c94b-4760-b90b-7502e63f9910: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/vdos/49adb33f-c94b-4760-b90b-7502e63f9910/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://vdos/49adb33f-c94b-4760-b90b-7502e63f9910/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/vdos/49adb33f-c94b-4760-b90b-7502e63f9910/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- 240616e576f22f95acea74a11b8e2bfef655440a24dbd09001e43201de2498fb
- Size of remote file:
- 6.78 kB
- SHA256:
- a39bdd8dbb3b8002cd9b9859b059577ec459e0b8bd4e24be24ca7bc513fb2d38
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