Instructions to use Aivesa/c61150d9-1501-429d-bbbf-bcd9cf65c7a1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Aivesa/c61150d9-1501-429d-bbbf-bcd9cf65c7a1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("dunzhang/stella_en_1.5B_v5") model = PeftModel.from_pretrained(base_model, "Aivesa/c61150d9-1501-429d-bbbf-bcd9cf65c7a1") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from Aivesa/c61150d9-1501-429d-bbbf-bcd9cf65c7a1: direct link, hf CLI and curl.
- Browser
- Download file 19.9 MB
-
https://huggingface.co/Aivesa/c61150d9-1501-429d-bbbf-bcd9cf65c7a1/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://Aivesa/c61150d9-1501-429d-bbbf-bcd9cf65c7a1/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Aivesa/c61150d9-1501-429d-bbbf-bcd9cf65c7a1/resolve/main/last-checkpoint/optimizer.pt
19.9 MB
- Xet hash:
- 3847422bb87f1294a97e41ed478c00a9376e8f324d899f5c8354ecbf30122256
- Size of remote file:
- 19.9 MB
- SHA256:
- c812cf5053e4c9b25bfb8f809da31e41961a2c3a93ff4d4a4269b2c5b2b3d8cd
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