README.md

metadata

license:
  - apache-2.0
  - bsd-3-clause
tags:
  - summarization
  - summary
  - booksum
  - long-document
  - long-form
  - tglobal-xl
  - XL
datasets:
  - kmfoda/booksum
metrics:
  - rouge
inference: false

long-t5-tglobal-xl-16384-book-summary: the 8-bit quantized version

This is an 8-bit quantized version of the pszemraj/long-t5-tglobal-xl-16384-book-summary model, The model has been compressed using bitsandbytes and can be loaded with low memory usage.

Refer to the original model for all details about the model architecture and training process. For more information on loading 8-bit models, refer to the 4.28.0 release information and the example repository.

• The total size of the model is only ~3.5 GB, much smaller than the original size.
• This allows for low-RAM loading, making it easier to use in memory-limited environments.
• bitsandbytes - AFAIK at time of writing - only works on GPU

Basic Usage

To use the model, install or upgrade transformers, accelerate, and bitsandbytes. Make sure to have transformers>=4.28.0 and bitsandbytes>0.37.2.

pip install -U -q transformers bitsandbytes accelerate

Load the model with AutoTokenizer and AutoModelForSeq2SeqLM:

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

model_name = "pszemraj/long-t5-tglobal-xl-16384-book-summary-8bit"
tokenizer = AutoTokenizer.from_pretrained(model_name)

model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

More information about long-t5-tglobal-xl-16384-book-summary

• This is an 8-bit quantized version of pszemraj/long-t5-tglobal-xl-16384-book-summary.
• It generalizes reasonably well to academic and narrative text, producing high-quality summaries.
• The XL checkpoint is used, resulting in even better summaries from a human evaluation perspective.
• A simple example/use case with the base model on ASR can be found here.
• A proof-of-concept example using the infamous Navy Seals copypasta demonstrates the model's ability to generate summaries from even short text inputs.