Instructions to use CogComp/l2d with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CogComp/l2d with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("CogComp/l2d") model = AutoModelForSeq2SeqLM.from_pretrained("CogComp/l2d", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from CogComp/l2d: direct link, hf CLI and curl.
- Browser
- Download file 2.95 GB
-
https://huggingface.co/CogComp/l2d/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://CogComp/l2d/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/CogComp/l2d/resolve/main/pytorch_model.bin
2.95 GB
- Xet hash:
- bcea7bc0ab4957111f40f54d4284143514cc2c613272329cb429db1583778029
- Size of remote file:
- 2.95 GB
- SHA256:
- 8faadf6b13e77fc0f29cb27bf477adc02007bc37ddf56da7884dc73bd8bc6e6d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.