Instructions to use EshAhm/LoRa-Python1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use EshAhm/LoRa-Python1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("EshAhm/LoRa-Python1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.safetensors from EshAhm/LoRa-Python1: direct link, hf CLI and curl.
- Browser
- Download file 83.9 MB
-
https://huggingface.co/EshAhm/LoRa-Python1/resolve/main/adapter_model.safetensors
- Command line
-
hf download hf://EshAhm/LoRa-Python1/adapter_model.safetensors
-
curl -L -o adapter_model.safetensors https://huggingface.co/EshAhm/LoRa-Python1/resolve/main/adapter_model.safetensors
83.9 MB
- Xet hash:
- 9d23559a495ce9a498a03c002b51814c26813372b254454f703e11bf32261287
- Size of remote file:
- 83.9 MB
- SHA256:
- 2746cc7bdcac3d6b58df64efac21ef3160762157b0ea2c2af8976c80df4b6ef8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.