Instructions to use silmiaulia/led-risalah_data_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmiaulia/led-risalah_data_v2 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="silmiaulia/led-risalah_data_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmiaulia/led-risalah_data_v2") model = AutoModelForSeq2SeqLM.from_pretrained("silmiaulia/led-risalah_data_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from silmiaulia/led-risalah_data_v2: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/silmiaulia/led-risalah_data_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://silmiaulia/led-risalah_data_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/silmiaulia/led-risalah_data_v2/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- 05a65eefa0cbd27e4b075221536787b47dd222ea5a2008a22f12faf407fe70a8
- Size of remote file:
- 5.3 kB
- SHA256:
- 557020ae8ca68806a6a67105fb65284c09bcb03f330ff16d8bcd6f48c296c936
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