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maestro: Streamlining Fine-Tuning for Multimodal Models like PaliGemma 2 and Florence-2
maestro is a powerful tool designed to accelerate the fine-tuning process for multimodal models. It encapsulates best practices, handling configuration, data loading, reproducibility, and training loop setup efficiently. The project currently offers ready-to-use recipes for popular vision-language models, including Florence-2, PaliGemma 2, and Qwen2.5-VL.
LLaMA-Factory: Unified Efficient Fine-Tuning for 100+ LLMs & VLMs
LLaMA-Factory is an open-source project offering a unified and efficient framework for fine-tuning over 100 large language models (LLMs) and vision-language models (VLMs). Recognized at ACL 2024, it provides a comprehensive suite of tools and algorithms for various training approaches. This repository simplifies the complex process of adapting powerful models for specific tasks with ease and scalability.