Instructions to use FlamingNeuron/llama381binstruct_summarize_short with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FlamingNeuron/llama381binstruct_summarize_short with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FlamingNeuron/llama381binstruct_summarize_short", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fb03e191770aba7a8b8126c2e2edc04b3434ba494ca1d1ae73807907c2a26665
- Size of remote file:
- 168 MB
- SHA256:
- b407df1b2973946b12fb32835ddfaa02ce7cdd2404d0d9eb34e7793e81f6047a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.