Instructions to use Lil-R/UMA_LLM_Engine_V2_Improved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lil-R/UMA_LLM_Engine_V2_Improved with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lil-R/UMA_LLM_Engine_V2_Improved", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 063fd78e65c4252fc64ec0ac57a9628cd6c1c378c2fb03b19e637bf098e4983c
- Size of remote file:
- 5.62 kB
- SHA256:
- 1d1144cce2a9ee019a0f8c1b92203e03658473fa2562ec2eda5cb8325380fa64
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.