Instructions to use Minchael/min_lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Minchael/min_lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("lmms-lab/llava-onevision-qwen2-7b-ov") model = PeftModel.from_pretrained(base_model, "Minchael/min_lora") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4bb8f5abb61c71384717e0e4186637e981ad908a878f81914e7ae0d641489427
- Size of remote file:
- 34 MB
- SHA256:
- cf58fa7fbda1ae17cbffddc13830ececa5b5ca7c3c6e14c40e0509e80670754e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.