Instructions to use microsoft/longcoder-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/longcoder-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/longcoder-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("microsoft/longcoder-base") model = AutoModelForMultimodalLM.from_pretrained("microsoft/longcoder-base") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 050cf3916644e160f5c8707814b4cd2bba8dbe8e74ce23acee099bfa8f743fc8
- Size of remote file:
- 1.39 kB
- SHA256:
- 76fa7dfb72c8619fa8b88f84eefb96e2e0491c8e6721c3c8e4b3a11ff7b2aae1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.