Instructions to use silmi224/led-risalah_data_v9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmi224/led-risalah_data_v9 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="silmi224/led-risalah_data_v9")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmi224/led-risalah_data_v9") model = AutoModelForSeq2SeqLM.from_pretrained("silmi224/led-risalah_data_v9", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from silmi224/led-risalah_data_v9: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/silmi224/led-risalah_data_v9/resolve/main/training_args.bin
- Command line
-
hf download hf://silmi224/led-risalah_data_v9/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/silmi224/led-risalah_data_v9/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 577d837a132bbe381fb5227295ec49488cf6807164fe436e297f1d5d19b235e5
- Size of remote file:
- 5.24 kB
- SHA256:
- ed0445cb74090e951b8f6a43f59c0e712b006983c40ac3070db075ee170600b2
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