Instructions to use silmi224/exp2-led-risalah_data_v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmi224/exp2-led-risalah_data_v3 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/exp2-led-risalah_data_v3")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmi224/exp2-led-risalah_data_v3") model = AutoModelForSeq2SeqLM.from_pretrained("silmi224/exp2-led-risalah_data_v3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from silmi224/exp2-led-risalah_data_v3: direct link, hf CLI and curl.
- Browser
- Download file 5.3 kB
-
https://huggingface.co/silmi224/exp2-led-risalah_data_v3/resolve/main/training_args.bin
- Command line
-
hf download hf://silmi224/exp2-led-risalah_data_v3/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/silmi224/exp2-led-risalah_data_v3/resolve/main/training_args.bin
5.3 kB
- Xet hash:
- bb593ff8e97edc0666c607cc7726a9052c1ae2ac53c74336c3dac96cb45c5f5f
- Size of remote file:
- 5.3 kB
- SHA256:
- a37af36b04dc3a9aae99057aeebe061d68748ac6127e1e5c5e7fed1a5f5cd1fa
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