Instructions to use kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Hermes-3-Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905: direct link, hf CLI and curl.
- Browser
- Download file 43.1 MB
-
https://huggingface.co/kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/kk-aivio/2e7d4628-1423-4d89-ad58-d4243e7b7905/resolve/main/last-checkpoint/optimizer.pt
43.1 MB
- Xet hash:
- 59afd5118d795ed9325f134bfea0b3ee319e46ebe98a30d7dede25469e3e216e
- Size of remote file:
- 43.1 MB
- SHA256:
- e07f600fb597088a902196bf21c0d0d7fba2f5afa28d2b50b63125f1fa85e4de
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