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/scheduler.pt from adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10/resolve/main/last-checkpoint/scheduler.pt
- Command line
-
hf download hf://adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10/last-checkpoint/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/adammandic87/8702c5ac-e5b9-418c-9c5c-367f6158cf10/resolve/main/last-checkpoint/scheduler.pt
1.06 kB
- Xet hash:
- bd02e030ec1f8ef12de002770ca66409af040c660fa9320ea57315e68a78bbc3
- Size of remote file:
- 1.06 kB
- SHA256:
- e69e2b49ea642509f0c688c16fb190b7cf27dac0a18903a5e2d1467d0343d8b8
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