Instructions to use ncauchi1/PointingDemo45k_adapter_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ncauchi1/PointingDemo45k_adapter_2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/root/.cache/huggingface/hub/models--Qwen--Qwen2.5-VL-3B-Instruct/snapshots/66285546d2b821cf421d4f5eb2576359d3770cd3") model = PeftModel.from_pretrained(base_model, "ncauchi1/PointingDemo45k_adapter_2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3ef1256bc71dfe024925f71d7d4ad28055164cc41f514b5e092c40de74dd9872
- Size of remote file:
- 8.15 kB
- SHA256:
- c00097113317f1759bf45d04f67b5372085294bdf73cc8f4c0f2d4d608523453
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.