flan-t5 - 技术专题

Swap GPT for any LLM by changing a single line of code. Xinference lets you run open-source, speech, and multimodal models on cloud, on-prem, or your laptop — all through one unified, production-ready inference API.

Toolkit for fine-tuning, ablating and unit-testing open-source LLMs.

This repository contains code for extending the Stanford Alpaca synthetic instruction tuning to existing instruction-tuned models such as Flan-T5.

[Preprint] Learning to Filter Context for Retrieval-Augmented Generaton

Build a Large Language Model (From Scratch) book and Finetuned Models

LLMs4OL:‌ Large Language Models for Ontology Learning

Official implementation of the paper "CoEdIT: Text Editing by Task-Specific Instruction Tuning" (EMNLP 2023)

[ACL 2024 Findings] Code implementation of Paper "Rethinking Negative Instances for Generative Named Entity Recognition"

This repository contains the code to train flan t5 with alpaca instructions and low rank adaptation.

Tools and our test data developed for the HackAPrompt 2023 competition

Empower your LLM to do more than you ever thought possible with these state-of-the-art prompt templates.

Fine-tuning of Flan-5T LLM for text classification 🤖 focuses on adapting a state-of-the-art language model to enhance its ability to classify text data.

A template Next.js app for running language models like FLAN-T5 with Replicate's API

The TABLET benchmark for evaluating instruction learning with LLMs for tabular prediction.

In this implementation, using the Flan T5 large language model, we performed the Text Classification task on the IMDB dataset and obtained a very good accuracy of 93%.

This project uses LLMs to generate music from text by understanding prompts, creating lyrics, determining genre, and composing melodies. It harnesses LLM capabilities to create songs based on text inputs through a multi-step approach.

Use AI to personify books, so that you can talk to them 🙊

This repository contains the lab work for Coursera course on "Generative AI with Large Language Models".

Training and fine-tuning flan-t5-small model based on provided text

Document Summarization App using large language model (LLM) and Langchain framework. Used a pre-trained T5 model and its tokenizer from Hugging Face Transformers library. Created a summarization pipeline to generate summary using model.