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:
- c73ca643f183f2e7f9961788b92f3c74f89eaaa2b601de82366ebfbbccdf87e7
- Size of remote file:
- 3.18 GB
- SHA256:
- 70e9628b614b67dc38759f775d84450b3444c576ef877aee32b6fe182b6e9db1
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