Instructions to use sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b") model = PeftModel.from_pretrained(base_model, "sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c: direct link, hf CLI and curl.
- Browser
- Download file 325 MB
-
https://huggingface.co/sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c/resolve/main/last-checkpoint/optimizer.pt
325 MB
- Xet hash:
- cadad41e6d712971fcc57a4fd44c2ca636550f28cd0a04c48b610a4174bfd6cb
- Size of remote file:
- 325 MB
- SHA256:
- 9bb6f50c7049107d8cb138f9f7d84beb89df460ef58ce14a75a8246f63b96c0a
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