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:
- 1f7a8ea53b9b593951835bb11d3286bfa8fcae253b2d06d062c1b0f2ec12024a
- Size of remote file:
- 86.7 MB
- SHA256:
- cd01c174f39522e2cd053cb2cb91c7b7dc6bf2dfd56908184c6c77ce965a97a7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.