Instructions to use cimol/e1cbbf9a-6ef4-4a89-8a71-d668dd19bc4f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/e1cbbf9a-6ef4-4a89-8a71-d668dd19bc4f 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/e1cbbf9a-6ef4-4a89-8a71-d668dd19bc4f") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from cimol/e1cbbf9a-6ef4-4a89-8a71-d668dd19bc4f: direct link, hf CLI and curl.
- Browser
- Download file 69.5 MB
-
https://huggingface.co/cimol/e1cbbf9a-6ef4-4a89-8a71-d668dd19bc4f/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://cimol/e1cbbf9a-6ef4-4a89-8a71-d668dd19bc4f/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/cimol/e1cbbf9a-6ef4-4a89-8a71-d668dd19bc4f/resolve/main/adapter_model.safetensors
69.5 MB
- Xet hash:
- 5bfac4e80735afa5ae3fbf9685d09627748be2b5135e88b5f3c94cf5874ac489
- Size of remote file:
- 69.5 MB
- SHA256:
- 094fb811e1832432588e86348f3c555f60640cdf5ace236c71be3078339393a6
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