Instructions to use Jeevesh8/t5-small_re-cogs_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/t5-small_re-cogs_4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/t5-small_re-cogs_4") model = AutoModelForSeq2SeqLM.from_pretrained("Jeevesh8/t5-small_re-cogs_4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Jeevesh8/t5-small_re-cogs_4: direct link, hf CLI and curl.
- Browser
- Download file 308 MB
-
https://huggingface.co/Jeevesh8/t5-small_re-cogs_4/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Jeevesh8/t5-small_re-cogs_4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Jeevesh8/t5-small_re-cogs_4/resolve/main/flax_model.msgpack
308 MB
- Xet hash:
- 5d53957a9aed71bf603090fd4939408221608676a1887b1ca639a69b8c335c88
- Size of remote file:
- 308 MB
- SHA256:
- 91578ae8772f30ea20640e8525aae43d703b4b14b73b2e59b935895928c6deab
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