Instructions to use adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.4") model = PeftModel.from_pretrained(base_model, "adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/training_args.bin from adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10: direct link, hf CLI and curl.
- Browser
- Download file 6.78 kB
-
https://huggingface.co/adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10/resolve/main/last-checkpoint/training_args.bin
- Command line
-
hf download hf://adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10/last-checkpoint/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10/resolve/main/last-checkpoint/training_args.bin
6.78 kB
- Xet hash:
- 49beb929dc879b779c5d5a2788be17c460efaa091865c4631b086bb6df2e23ba
- Size of remote file:
- 6.78 kB
- SHA256:
- 3d266b22502023e76b024d89601fad89bd9fe4b4a2dbc83527f6b478ba5a5253
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