Instructions to use aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Vikhrmodels/Vikhr-7B-instruct_0.4") model = PeftModel.from_pretrained(base_model, "aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4: direct link, hf CLI and curl.
- Browser
- Download file 336 MB
-
https://huggingface.co/aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4/resolve/main/adapter_model.bin
- Command line
-
hf download hf://aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/aleegis/50089649-6ad2-4906-b3ec-a7a2fc7302c4/resolve/main/adapter_model.bin
336 MB
- Xet hash:
- 1ce42d7feee0aa87284627e47d2000dc20baaa3ceef03212abd3376decf855f8
- Size of remote file:
- 336 MB
- SHA256:
- cf3389d954f90dea191b0fe46d09b970eab642104f7ebaad550122290a306468
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.