# Flan-t5-xl-gguf Open-source AI Model - Fine-tuned for over a Thousand Multilingual Tasks, with Superior Performance

Flan T5 Xl Gguf

Developed by deepfile

FLAN-T5 is the instruction-fine-tuned version of the T5 model, optimized through fine-tuning on over 1,000 multilingual tasks, delivering superior performance at the same parameter count.

Large Language Model Supports Multiple LanguagesOpen Source License:Apache-2.0 #Multi-task instruction fine-tuning #Multilingual understanding #Enhanced logical reasoning

Downloads 61

Release Time : 12/9/2023

Model Overview

FLAN-T5 XL is an instruction-fine-tuned model based on the T5 architecture, supporting multiple languages and tasks with strong few-shot learning capabilities.

Model Features

Multi-task instruction fine-tuning

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

Few-shot learning capability

Demonstrates strong few-shot performance even compared to larger models

Multilingual support

Supports processing in multiple languages including English, French, German, Romanian

Model Capabilities

Text generation

Translation

Question answering

Logical reasoning

Scientific knowledge answering

Mathematical reasoning

Boolean expression evaluation

Use Cases

Natural Language Processing

Multilingual translation

Supports text translation between multiple languages

Complex QA

Capable of answering knowledge-based questions requiring reasoning

Education

Math problem solving

Can solve mathematical reasoning problems

Scientific knowledge answering

Answers various scientific questions

🚀 Model Card for FLAN - T5 XL

FLAN - T5 XL is an enhanced version of T5, fine - tuned on over 1000 additional tasks across multiple languages, offering superior performance with the same number of parameters.

🚀 Quick Start

Supported Languages

English
French
Romanian
German
Multilingual

Widget Examples

Example Title	Text
Translation	"Translate to German: My name is Arthur"
Question Answering	"Please answer to the following question. Who is going to be the next Ballon d'or?"
Logical reasoning	"Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
Scientific knowledge	"Please answer the following question. What is the boiling point of Nitrogen?"
Yes/no question	"Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
Reasoning task	"Answer the following yes/no question by reasoning step - by - step. Can you write a whole Haiku in a single tweet?"
Boolean Expressions	"Q: ( False or not False or False ) is? A: Let's think step by step"
Math reasoning	"The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
Premise and hypothesis	"Premise: At my age you will probably have learnt one lesson. Hypothesis: It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?"

Datasets

svakulenk0/qrecc
taskmaster2
djaym7/wiki_dialog
deepmind/code_contests
lambada
gsm8k
aqua_rat
esnli
quasc
qed

License

apache - 2.0

TL;DR

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.

Model Details

Model Description

The details are in the original [google/flan - t5 - xl](https://huggingface.co/google/flan - t5 - xl)

Model Architecture

Featured Recommended AI Models

Empowering the Future, Your AI Solution Knowledge Base

English 简体中文繁體中文にほんご