declare-lab/Auto-Scaling
[Arxiv 2024] Official Implementation of the paper: "Towards Robust Instruction Tuning on Multimodal Large Language Models"
This tool helps AI researchers and practitioners improve the performance of their Multimodal Large Language Models (MLLMs) by automatically generating a much larger set of training instructions from a small initial set. It takes existing instruction datasets and expands them up to 30 times, producing a more robust and diverse dataset for model fine-tuning. This is for AI/ML researchers and data scientists who train and fine-tune advanced AI models.
Use this if you need to fine-tune Multimodal Large Language Models for better performance but have a limited amount of instruction data.
Not ideal if you are not working with multimodal large language models or instruction tuning, or if you already have sufficiently large and diverse instruction datasets.
Stars
9
Forks
1
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Dec 05, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/transformers/declare-lab/Auto-Scaling"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
DaoD/INTERS
This is the repository for our paper "INTERS: Unlocking the Power of Large Language Models in...
declare-lab/instruct-eval
This repository contains code to quantitatively evaluate instruction-tuned models such as Alpaca...
Haiyang-W/TokenFormer
[ICLR2025 Spotlightš„] Official Implementation of TokenFormer: Rethinking Transformer Scaling...
hkust-nlp/deita
Deita: Data-Efficient Instruction Tuning for Alignment [ICLR2024]
kehanlu/DeSTA2
Code and model for ICASSP 2025 Paper "Developing Instruction-Following Speech Language Model...