Instructions to use Javel/linkedin_post_t5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Javel/linkedin_post_t5 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Javel/linkedin_post_t5") model = AutoModelForSeq2SeqLM.from_pretrained("Javel/linkedin_post_t5", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Javel/linkedin_post_t5: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/Javel/linkedin_post_t5/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Javel/linkedin_post_t5/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Javel/linkedin_post_t5/resolve/main/flax_model.msgpack
892 MB
- Xet hash:
- 675b05446cce57b835ec37529414efe3b2fbdb1bf4433a5e2c312e717fd77210
- Size of remote file:
- 892 MB
- SHA256:
- 19524d84b0cd161375484e8cb1dd286fc3ccaf70c8e9778f44bb20ff908c94c3
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