Instructions to use cimol/ad3bc1b8-ba08-4f1f-98a9-69fdbae7884b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/ad3bc1b8-ba08-4f1f-98a9-69fdbae7884b 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, "cimol/ad3bc1b8-ba08-4f1f-98a9-69fdbae7884b") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/rng_state.pth from cimol/ad3bc1b8-ba08-4f1f-98a9-69fdbae7884b: direct link, hf CLI and curl.
- Browser
- Download file 14.2 kB
-
https://huggingface.co/cimol/ad3bc1b8-ba08-4f1f-98a9-69fdbae7884b/resolve/main/last-checkpoint/rng_state.pth
- Command line
-
hf download hf://cimol/ad3bc1b8-ba08-4f1f-98a9-69fdbae7884b/last-checkpoint/rng_state.pth
-
curl -L -o rng_state.pth https://huggingface.co/cimol/ad3bc1b8-ba08-4f1f-98a9-69fdbae7884b/resolve/main/last-checkpoint/rng_state.pth
14.2 kB
- Xet hash:
- 74c33e2c10d80420d6bed0d43f76e828918b2af6c6c82b4bbc6cf47a4d7d1bb7
- Size of remote file:
- 14.2 kB
- SHA256:
- c2cf8928711bed18ac62e7218396d85b35ab45280ef8f10bdeb3d559b186da4a
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