Instructions to use midas/gupshup_h2e_t5_mtl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use midas/gupshup_h2e_t5_mtl with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("midas/gupshup_h2e_t5_mtl") model = AutoModelForSeq2SeqLM.from_pretrained("midas/gupshup_h2e_t5_mtl", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from midas/gupshup_h2e_t5_mtl: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/midas/gupshup_h2e_t5_mtl/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://midas/gupshup_h2e_t5_mtl/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/midas/gupshup_h2e_t5_mtl/resolve/main/pytorch_model.bin
892 MB
- Xet hash:
- a783737e5f4857d33d509b7d008251e3f975673494fc069c67f2f3d8fd6f57ff
- Size of remote file:
- 892 MB
- SHA256:
- ac26d35b597526410653c18dfaf70074b5730c7fdae334f4dba790cdc128f653
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