Instructions to use silmi224/led-risalah_data_v7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmi224/led-risalah_data_v7 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_v7")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmi224/led-risalah_data_v7") model = AutoModelForSeq2SeqLM.from_pretrained("silmi224/led-risalah_data_v7", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from silmi224/led-risalah_data_v7: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/silmi224/led-risalah_data_v7/resolve/main/training_args.bin
- Command line
-
hf download hf://silmi224/led-risalah_data_v7/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/silmi224/led-risalah_data_v7/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 8f4633090472c45f8e67e5717a51a39b7f81bf8adf3169cdc833d851ba0d9093
- Size of remote file:
- 4.73 kB
- SHA256:
- 86e78398ab1e5b10bcb3ced4bf09e7a1b3dbbbc614c1e15b8a26b094929bf095
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