Instructions to use thakkkkkk/0ba84ccc-c75c-4a03-ad42-26e98fe5b5f1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use thakkkkkk/0ba84ccc-c75c-4a03-ad42-26e98fe5b5f1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-1.1-2b-it") model = PeftModel.from_pretrained(base_model, "thakkkkkk/0ba84ccc-c75c-4a03-ad42-26e98fe5b5f1") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from thakkkkkk/0ba84ccc-c75c-4a03-ad42-26e98fe5b5f1: direct link, hf CLI and curl.
- Browser
- Download file 39.3 MB
-
https://huggingface.co/thakkkkkk/0ba84ccc-c75c-4a03-ad42-26e98fe5b5f1/resolve/main/adapter_model.bin
- Command line
-
hf download hf://thakkkkkk/0ba84ccc-c75c-4a03-ad42-26e98fe5b5f1/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/thakkkkkk/0ba84ccc-c75c-4a03-ad42-26e98fe5b5f1/resolve/main/adapter_model.bin
39.3 MB
- Xet hash:
- eca151edb3f54b7ab8d246a0ea532085c70eeacf18f6cc8de1c6bf7572c6888f
- Size of remote file:
- 39.3 MB
- SHA256:
- fd1f8f9908b2cbd7344b38b443c23d3b0f28d632ffc7ad2daf2310da042e999e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.