Instructions to use dusersad12/OrionLM-CheckpointRepo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dusersad12/OrionLM-CheckpointRepo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="dusersad12/OrionLM-CheckpointRepo")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("dusersad12/OrionLM-CheckpointRepo") model = AutoModel.from_pretrained("dusersad12/OrionLM-CheckpointRepo", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from dusersad12/OrionLM-CheckpointRepo: direct link, hf CLI and curl.
- Browser
- Download file 24 Bytes
-
https://huggingface.co/dusersad12/OrionLM-CheckpointRepo/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://dusersad12/OrionLM-CheckpointRepo/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/dusersad12/OrionLM-CheckpointRepo/resolve/main/pytorch_model.bin
24 Bytes
- Xet hash:
- 8ecbb1cabe75d64f97625381f7c7d54f627f3227ec680b0163904be798a3dc81
- Size of remote file:
- 24 Bytes
- SHA256:
- afea34d65d91a71ca6e9d02941bb48d12278700281f1719f1d6b2c4ec1820068
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.