Instructions to use cimol/4cfcb15c-3ca3-42d9-990a-c70b95428168 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/4cfcb15c-3ca3-42d9-990a-c70b95428168 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM2-360M") model = PeftModel.from_pretrained(base_model, "cimol/4cfcb15c-3ca3-42d9-990a-c70b95428168") - Notebooks
- Google Colab
- Kaggle
Download last-checkpoint/optimizer.pt from cimol/4cfcb15c-3ca3-42d9-990a-c70b95428168: direct link, hf CLI and curl.
- Browser
- Download file 71.1 MB
-
https://huggingface.co/cimol/4cfcb15c-3ca3-42d9-990a-c70b95428168/resolve/main/last-checkpoint/optimizer.pt
- Command line
-
hf download hf://cimol/4cfcb15c-3ca3-42d9-990a-c70b95428168/last-checkpoint/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/cimol/4cfcb15c-3ca3-42d9-990a-c70b95428168/resolve/main/last-checkpoint/optimizer.pt
71.1 MB
- Xet hash:
- 2014e256bc2e3d4af7fbca2b8506f6f073575fc8f8eb19b58a5fd8dcf7b58494
- Size of remote file:
- 71.1 MB
- SHA256:
- 122d903c665cd431f2c6e8423595912585c5fba459fb0af8de39b5f25e66e937
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