Instructions to use SyedShaheer/SUMMARIZING_STRESSED_MODEL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SyedShaheer/SUMMARIZING_STRESSED_MODEL 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="SyedShaheer/SUMMARIZING_STRESSED_MODEL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SyedShaheer/SUMMARIZING_STRESSED_MODEL") model = AutoModelForSeq2SeqLM.from_pretrained("SyedShaheer/SUMMARIZING_STRESSED_MODEL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from SyedShaheer/SUMMARIZING_STRESSED_MODEL: direct link, hf CLI and curl.
- Browser
- Download file 2.11 MB
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https://huggingface.co/SyedShaheer/SUMMARIZING_STRESSED_MODEL/resolve/main/tokenizer.json
- Command line
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hf download hf://SyedShaheer/SUMMARIZING_STRESSED_MODEL/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/SyedShaheer/SUMMARIZING_STRESSED_MODEL/resolve/main/tokenizer.json
2.11 MB
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