Instructions to use silmi224/led-risalah_data_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmi224/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="silmi224/led-risalah_data_v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmi224/led-risalah_data_v2") model = AutoModelForSeq2SeqLM.from_pretrained("silmi224/led-risalah_data_v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from silmi224/led-risalah_data_v2: direct link, hf CLI and curl.
- Browser
- Download file 4.73 kB
-
https://huggingface.co/silmi224/led-risalah_data_v2/resolve/main/training_args.bin
- Command line
-
hf download hf://silmi224/led-risalah_data_v2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/silmi224/led-risalah_data_v2/resolve/main/training_args.bin
4.73 kB
- Xet hash:
- 416ca50d08ccefe2b9b05b34762f318bf7641f53f476672139b781187bffb6b1
- Size of remote file:
- 4.73 kB
- SHA256:
- bb02c0b12ac7b4a1b203a33bb9f7e802a45ee7527f656dce5b85c2e2442c7f4f
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