Instructions to use Kaludi/Quick-Summarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaludi/Quick-Summarization 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="Kaludi/Quick-Summarization")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Kaludi/Quick-Summarization") model = AutoModelForSeq2SeqLM.from_pretrained("Kaludi/Quick-Summarization", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 814bab40d4ca2aa3e5eea515b0b311746044bba3f5b1a8d6cb7cb43dbf8dbd06
- Size of remote file:
- 2.28 GB
- SHA256:
- 5bdd3074cbf779f9fec306d91cc22ee41b9f6122a9a6e443f2692d7620e1968f
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