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/scheduler.pt from sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c/resolve/main/last-checkpoint/scheduler.pt
- Command line
-
hf download hf://sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c/last-checkpoint/scheduler.pt
-
curl -L -o scheduler.pt https://huggingface.co/sn56z0/f92f7a48-1095-43e5-8f53-0f4dc97e400c/resolve/main/last-checkpoint/scheduler.pt
1.06 kB
- Xet hash:
- aa21f83f613f92ecebe355e2e04e0e4666d2ae836dd6d8768d456948fe2ae214
- Size of remote file:
- 1.06 kB
- SHA256:
- d2d754412c61116546142914503e7369d0cc35d3c380a07e5218f595d76b6d96
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