Fine-tuning MLLMs: A Deep Dive into Multi-turn Chat Datasets
research#mllm📝 Blog|Analyzed: Feb 18, 2026 15:33•
Published: Feb 18, 2026 15:17
•1 min read
•r/deeplearningAnalysis
This is an exciting exploration into fine-tuning a Multimodal Large Language Model (MLLM) using a multi-turn chat dataset. The research focuses on the crucial challenges of dataset construction and dataloader classes for effective training, which paves the way for advanced Generative AI applications. This work promises to unlock new capabilities in interactive AI systems!
Key Takeaways
- •The research focuses on fine-tuning Multimodal Large Language Models (MLLMs) for multi-turn conversational tasks.
- •The core challenge lies in constructing the Dataset and Dataloader classes, especially handling labels.
- •The project uses the LLaVA-Instruct dataset, a multi-turn chat dataset, for fine-tuning.
Reference / Citation
View Original"I'm trying to fine-tune an MLLM on LLaVA-Instruct dataset (which is a multi-turn chat dataset). I am strugling to build the Dataset and Dataloader classes to train the model, specially because of how to build the labels."