TinyGPT-V: Efficient Multimodal Large Language Model via Small Backbones (2312.16862v3)

Abstract: In recent years, multimodal LLMs (MLLMs) such as GPT-4V have demonstrated remarkable advancements, excelling in a variety of vision-language tasks. Despite their prowess, the closed-source nature and computational demands of such models limit their accessibility and applicability. This study introduces TinyGPT-V, a novel open-source MLLM, designed for efficient training and inference across various vision-language tasks, including image captioning (IC) and visual question answering (VQA). Leveraging a compact yet powerful architecture, TinyGPT-V integrates the Phi-2 LLM with pre-trained vision encoders, utilizing a unique mapping module for visual and linguistic information fusion. With a training regimen optimized for small backbones and employing a diverse dataset amalgam, TinyGPT-V requires significantly lower computational resources 24GB for training and as little as 8GB for inference without compromising on performance. Our experiments demonstrate that TinyGPT-V, with its LLM 2.8 billion parameters, achieves comparable results in VQA and image inference tasks to its larger counterparts while being uniquely suited for deployment on resource-constrained devices through innovative quantization techniques. This work not only paves the way for more accessible and efficient MLLMs but also underscores the potential of smaller, optimized models in bridging the gap between high performance and computational efficiency in real-world applications. Additionally, this paper introduces a new approach to multimodal LLMs using smaller backbones. Our code and training weights are available in the supplementary material.

• The paper introduces TinyGPT-V, an efficient multimodal model combining a 2.8B-parameter Phi-2 with pre-trained vision modules to balance performance and resource use.
• The paper details a rigorous four-stage training process—including warm-up, pre-training, human-like, and multi-task learning—that optimizes the model's VQA capabilities.
• The paper evidences TinyGPT-V's efficacy with leading benchmarks, such as a 53.2% VSR score, making high-performance deployment feasible on limited GPU hardware.

Overview of TinyGPT-V: Efficient Multimodal LLM via Small Backbones

The paper "TinyGPT-V: Efficient Multimodal LLM via Small Backbones" by Yuan, Li, and Sun presents an in-depth exploration and development of TinyGPT-V, a multimodal LLM (MLLM) designed for practical and efficient deployment without compromising performance. This paper is premised on the pressing need to balance computational efficiency with robust multimodal capabilities in the face of commercially restrained, large, and resource-intensive models like GPT-4V.

Core Concept and Methodology

The central innovation of TinyGPT-V lies in its efficient architecture, which integrates a small but capable LLM, Phi-2, with pre-trained vision modules from BLIP-2 or CLIP. This architecture limits the computational requirement to a 24G GPU for training and an 8G device for inference, thus making local deployment feasible. Phi-2, with its 2.8 billion parameters, is significantly smaller compared to the traditionally larger models typically employed in similar tasks, yet maintains competitive performance due to its novel quantization process.

The methodology section of the paper outlines a meticulous four-stage training process:

1. Warm-up Training: Utilizing large-scale image-text pairs, this stage aligns the Phi-2 model with visual input, facilitating initial multimodal interaction capabilities.
2. Pre-Training: This phase emphasizes refining the LoRA (Low-Rank Adaptation) modules to enhance multimodal understanding.
3. Human-like Learning: Fine-tuning with specific instruction datasets that encourage the model to generate natural, coherent text responses akin to human interaction.
4. Multi-task Learning: This stage aims to generalize the model’s capabilities across various multimodal tasks, incorporating diverse datasets to enrich its performance metrics.

Experimental Evaluation

The evaluation benchmarks in the paper highlight the competency of TinyGPT-V against models with larger parameter sets. Table 1 showcases comparative performance metrics across multiple visual question-answering (VQA) datasets:

• In the VSR (Visual Spatial Reasoning) test, TinyGPT-V achieved a leading score of 53.2%, outperforming other heavy models like BLIP-2 and LLaVA, which validates its efficiency.
• It also exhibits strong results in GQA (33.6%), IconVQ (43.3%), VizWiz (24.8%), and HM (53.2%), demonstrating substantial capabilities even as it lags behind in some metrics compared to its 13B parameters competitors like InstructBLIP and MiniGPT-4.

Practical and Theoretical Implications

Practically, TinyGPT-V represents a significant step towards democratizing access to advanced MLLMs by reducing the requirements for computational resources. This opens up potential applications in sectors where deployment has been previously constrained by hardware limitations.

Theoretically, the paper pushes the frontier in several ways:

• It questions the necessity of large-scale parameters for achieving high performance in multimodal tasks, suggesting that more efficient models can achieve similar or even superior results.
• The inclusion of additional normalization layers in training smaller models highlights a critical understanding of the challenges associated with scaling down LLMs effectively.

Future Directions

This development invites further exploration in the creation of smaller yet highly efficient MLLMs. The success of TinyGPT-V suggests several future research avenues:

• Investigating similar architectures across different modalities and tasks can elucidate the upper bounds of efficiency versus performance.
• Extending the normalization and training techniques to other smaller models may yield more nuanced insights into their adaptation and generalization potentials.
• An in-depth analysis of the quantization process used could reveal optimizations applicable across a broader array of language and vision models.

Conclusion

The research presented in "TinyGPT-V: Efficient Multimodal LLM via Small Backbones" establishes a strong foundation for developing accessible, high-performance MLLMs. By leveraging a smaller, efficient model like Phi-2, coupled with robust pre-trained vision modules, TinyGPT-V achieves a balance that has been elusive in the domain. This work is poised to inspire future innovations in the field of multimodal learning, promoting the proliferation of highly capable yet computationally frugal models.