Instructions to use silmi224/led-risalah_data_v14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silmi224/led-risalah_data_v14 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_v14")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("silmi224/led-risalah_data_v14") model = AutoModelForSeq2SeqLM.from_pretrained("silmi224/led-risalah_data_v14", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from silmi224/led-risalah_data_v14: direct link, hf CLI and curl.
- Browser
- Download file 5.24 kB
-
https://huggingface.co/silmi224/led-risalah_data_v14/resolve/main/training_args.bin
- Command line
-
hf download hf://silmi224/led-risalah_data_v14/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/silmi224/led-risalah_data_v14/resolve/main/training_args.bin
5.24 kB
- Xet hash:
- 50a9a06b5c5ee3a585e5b8c97b5883b2c583eae809aa6ac2ee2914c8aa134532
- Size of remote file:
- 5.24 kB
- SHA256:
- e4066a336bfc42d82f5d54b80e1233e998a74e798411bceeb18ad88500038073
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