Instructions to use Lennard-Heuer/DS_Trained_LLM_Task2_2025_11_21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lennard-Heuer/DS_Trained_LLM_Task2_2025_11_21 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lennard-Heuer/DS_Trained_LLM_Task2_2025_11_21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f46c4f57c22fc7f0c69148e2aa714a13f4f633d689778283fd2ce35a693fb78f
- Size of remote file:
- 11.4 MB
- SHA256:
- d6187b61f7ea74894101c4c4fe4b815016546fd8c67df3672ea557ffcea365d9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.