Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use athugodage/T5-RLS2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use athugodage/T5-RLS2000 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("athugodage/T5-RLS2000") model = AutoModelForSeq2SeqLM.from_pretrained("athugodage/T5-RLS2000", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from athugodage/T5-RLS2000: direct link, hf CLI and curl.
- Browser
- Download file 2.95 GB
-
https://huggingface.co/athugodage/T5-RLS2000/resolve/db4d04fdf41d6861dc8cc853c8a35100e8084827/pytorch_model.bin
- Command line
-
hf download hf://athugodage/T5-RLS2000@db4d04fdf41d6861dc8cc853c8a35100e8084827/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/athugodage/T5-RLS2000/resolve/db4d04fdf41d6861dc8cc853c8a35100e8084827/pytorch_model.bin
2.95 GB
- Xet hash:
- ad30005b8942aa5c6311c76cfc9a52808ee62cce80aabdb68aa039fb41eb3ae4
- Size of remote file:
- 2.95 GB
- SHA256:
- cfc195396e426702f7cd9f417f514da9af1fad9139ed8dcc95acb48be905f5ee
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.