Transformers
PyTorch
TensorBoard
t5
text2text-generation
Generated from Trainer
Eval Results (legacy)
text-generation-inference
Instructions to use ethansimrm/test_t5_small_example_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ethansimrm/test_t5_small_example_3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("ethansimrm/test_t5_small_example_3") model = AutoModelForSeq2SeqLM.from_pretrained("ethansimrm/test_t5_small_example_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from ethansimrm/test_t5_small_example_3: direct link, hf CLI and curl.
- Browser
- Download file 3.77 kB
-
https://huggingface.co/ethansimrm/test_t5_small_example_3/resolve/main/training_args.bin
- Command line
-
hf download hf://ethansimrm/test_t5_small_example_3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/ethansimrm/test_t5_small_example_3/resolve/main/training_args.bin
3.77 kB
- Xet hash:
- ceae3e04348759ee02c119ead9ef87f9e6f87bf2128d4773864e2578642c8437
- Size of remote file:
- 3.77 kB
- SHA256:
- 56f805b90d755ddd3be5fe7bcabb60908f5e5c4302a46e346535c08f7cd15e2e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.