Instructions to use silmi224/led-risalah_data_v11 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmi224/led-risalah_data_v11 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_v11")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmi224/led-risalah_data_v11") model = AutoModelForSeq2SeqLM.from_pretrained("silmi224/led-risalah_data_v11", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from silmi224/led-risalah_data_v11: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/silmi224/led-risalah_data_v11/resolve/main/training_args.bin
- Command line
-
hf download hf://silmi224/led-risalah_data_v11/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/silmi224/led-risalah_data_v11/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 0f32f18e636e5072159d714b8d24484bd378109c6da07e39ea6692749c384ad1
- Size of remote file:
- 5.24 kB
- SHA256:
- 2293e0e68ad51cc5b27b153192c0b75f2b2302e59e5a68400b8568022716e237
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