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
| { | |
| "_from_model_config": true, | |
| "decoder_start_token_id": 0, | |
| "early_stopping": true, | |
| "encoder_no_repeat_ngram_size": 4, | |
| "eos_token_id": 1, | |
| "length_penalty": 0.8, | |
| "max_length": 1024, | |
| "min_length": 8, | |
| "no_repeat_ngram_size": 3, | |
| "num_beams": 2, | |
| "pad_token_id": 0, | |
| "transformers_version": "4.28.1" | |
| } | |