Summarization
Transformers
PyTorch
Safetensors
longt5
text2text-generation
summary
booksum
long-document
long-form
tglobal-xl
XL
8bit
quantized
8-bit precision
Instructions to use pszemraj/long-t5-tglobal-xl-16384-book-summary-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pszemraj/long-t5-tglobal-xl-16384-book-summary-8bit 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="pszemraj/long-t5-tglobal-xl-16384-book-summary-8bit")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("pszemraj/long-t5-tglobal-xl-16384-book-summary-8bit") model = AutoModelForSeq2SeqLM.from_pretrained("pszemraj/long-t5-tglobal-xl-16384-book-summary-8bit", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6e0fd23ab8db49788ac78ad87e51e12798628facae0c8709ca69b7c01e5b1088
- Size of remote file:
- 3.18 GB
- SHA256:
- 2750041b02266d3f8507b3962120c46791bbb2507f39999c4904bc2d914f88e1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.