Instructions to use Labib11/PMC_GIST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Labib11/PMC_GIST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Labib11/PMC_GIST")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Labib11/PMC_GIST") model = AutoModel.from_pretrained("Labib11/PMC_GIST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Labib11/PMC_GIST: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/Labib11/PMC_GIST/resolve/main/training_args.bin
- Command line
-
hf download hf://Labib11/PMC_GIST/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Labib11/PMC_GIST/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- c5aa5001217e6dc382b921fcc5e040ffaa6ba9b555d743ab37078aec6eb85392
- Size of remote file:
- 5.24 kB
- SHA256:
- 820fa11ec31349a8106bbea6d96f58c217ad08485c8d03bdce5ba8d89d670319
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