Instructions to use quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Llama-3.2-3B") model = PeftModel.from_pretrained(base_model, "quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b: direct link, hf CLI and curl.
- Browser
- Download file 25.2 MB
-
https://huggingface.co/quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/quannh197/8e0fd235-c40a-4533-b8a4-14cc3cb82a5b/resolve/main/last-checkpoint/optimizer.pt
25.2 MB
- Xet hash:
- ae39615b5b4add56d561e4f0c1881ebc933705a1e8979f2953d5a21157dc04a8
- Size of remote file:
- 25.2 MB
- SHA256:
- 15aed647afd692eb998c4653549e31203a724dc0b5b8fbfb1e08e37cba3bf795
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