Instructions to use lightbansal/metadata_postprocess with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lightbansal/metadata_postprocess with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="lightbansal/metadata_postprocess")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("lightbansal/metadata_postprocess") model = AutoModelForSeq2SeqLM.from_pretrained("lightbansal/metadata_postprocess", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from lightbansal/metadata_postprocess: direct link, hf CLI and curl.
- Browser
- Download file 2.28 GB
-
https://huggingface.co/lightbansal/metadata_postprocess/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://lightbansal/metadata_postprocess/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/lightbansal/metadata_postprocess/resolve/main/pytorch_model.bin
2.28 GB
- Xet hash:
- e1b9547db4e6ed26f87b3de2c632f9ffebbccd93d10b15d980e04bf907742421
- Size of remote file:
- 2.28 GB
- SHA256:
- 74ad60f733047b448534163fcdd772331bdc5e686ee389f4fb2ec118e6d3a6bd
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