FLAN-T5 XXL Open-Source AI Model - Fine-Tuned on Over 1,000 Tasks, with Enhanced Performance in Multilingual Support

Flan T5 Xxl Sharded Fp16

Developed by philschmid

FLAN-T5 XXL is a variant of Google's T5 model, fine-tuned on over 1,000 additional tasks, supports multiple languages, and outperforms the original T5 model.

Large Language Model

Transformers

Open Source License:Apache-2.0 #Multilingual instruction fine-tuning #Large-scale few-shot learning #Text generation optimization

Downloads 531

Release Time : 1/27/2023

Model Overview

This is a forked version of google/flan-t5-xxl, implementing a custom handler.py as an example for using t5-11b on a single NVIDIA A10G via an inference endpoint.

Model Features

Multi-task fine-tuning

Fine-tuned on over 1,000 additional tasks, covering multiple languages and task types

Quantized version

Supports running on a single NVIDIA A10G GPU, reducing hardware requirements

Multilingual support

Supports processing and generation in over 60 languages

Superior performance

Outperforms the original T5 model with the same parameter scale

Model Capabilities

Text generation

Question answering systems

Multilingual translation

Instruction understanding

Text summarization

Use Cases

Natural language processing

Multilingual question answering system

Build an intelligent question answering system supporting multiple languages

Achieves 75.2% accuracy on five-shot MMLU

Text summarization

Automatically generate summaries of articles or documents

Machine translation

Supports mutual translation between multiple languages

🚀 FORK of FLAN-T5 XXL

This is a fork of google/flan-t5-xxl. It implements a custom handler.py as an example for using t5-11b with inference-endpoints on a single NVIDIA A10G.

🚀 Quick Start

You can deploy the flan-t5-xxl with a 1-click. Since we are using the "quantized" version, we can switch our instance type to "GPU [medium] · 1x Nvidia A10G".

createEndpoint

✨ Features

If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract:

Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.

Disclaimer: Content from this model card has been written by the Hugging Face team, and parts of it were copy pasted from the T5 model card.

📚 Documentation

Model Details

Model Description

Property	Details
Model Type	Language model
Language(s) (NLP)	English, Spanish, Japanese, Persian, Hindi, French, Chinese, Bengali, Gujarati, German, Telugu, Italian, Arabic, Polish, Tamil, Marathi, Malayalam, Oriya, Panjabi, Portuguese, Urdu, Galician, Hebrew, Korean, Catalan, Thai, Dutch, Indonesian, Vietnamese, Bulgarian, Filipino, Central Khmer, Lao, Turkish, Russian, Croatian, Swedish, Yoruba, Kurdish, Burmese, Malay, Czech, Finnish, Somali, Tagalog, Swahili, Sinhala, Kannada, Zhuang, Igbo, Xhosa, Romanian, Haitian, Estonian, Slovak, Lithuanian, Greek, Nepali, Assamese, Norwegian
License	Apache 2.0
Related Models	All FLAN-T5 Checkpoints
Original Checkpoints	All Original FLAN-T5 Checkpoints
Resources for more information	Research paper, GitHub Repo, Hugging Face FLAN-T5 Docs (Similar to T5)

📄 License

This model is licensed under the Apache 2.0 license.

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