Instructions to use dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/SmolLM-135M-Instruct") model = PeftModel.from_pretrained(base_model, "dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9: direct link, hf CLI and curl.
- Browser
- Download file 19.7 MB
-
https://huggingface.co/dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9/resolve/main/adapter_model.bin
- Command line
-
hf download hf://dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/dimasik1987/6cdd4d79-fbef-4c7a-b2c9-6a4ae8b777f9/resolve/main/adapter_model.bin
19.7 MB
- Xet hash:
- e5554e32a4bea65aaae8e375dcb869ddceb4c821c931c5dd39d219e9969027aa
- Size of remote file:
- 19.7 MB
- SHA256:
- a93397a9b36f389eac171a37c8b36deb27303f44f17573f5a64fcdb4205561b1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.