yongxianwei/Qwen2.5-VL-32B-Grounding
Qwen2.5-VL-32B-Grounding is a 32-billion parameter vision-language model developed by Yongxian Wei and collaborators, fine-tuned from Qwen2.5-VL-32B-Instruct. It serves as a specialized "Grounding" expert within the OptMerge MLLM merging benchmark, focusing on tasks requiring precise object localization and referring expression comprehension. This model is designed to be merged with other capability-specific experts to create a unified multimodal LLM without additional training data, making it ideal for research into efficient multimodal model integration.
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Overview
Qwen2.5-VL-32B-Grounding is a 32-billion parameter vision-language model, specifically fine-tuned as a "Grounding" expert. It is part of the OptMerge MLLM merging benchmark, which explores unifying multimodal LLM capabilities through model merging. This model is one of five distinct experts (VQA, Geometry, Chart, OCR, Grounding) derived from the same base model, Qwen/Qwen2.5-VL-32B-Instruct, allowing them to be combined without requiring new training data.
Key Capabilities
- Specialized Grounding: Optimized for tasks that involve grounding natural language descriptions to specific regions within images, such as RefCOCO, RefCOCO+, and RefCOCOg.
- Model Merging Component: Designed to be merged with other expert models using the OptMerge framework, enabling the creation of a single, comprehensive multimodal LLM from specialized components.
- Full Weights: This repository provides the full fine-tuned weights, eliminating the need for PEFT adapters for direct loading and usage.
Good For
- Research in Multimodal Model Merging: Ideal for researchers exploring efficient methods to combine specialized multimodal models, particularly within the context of the OptMerge framework.
- Grounding-Specific Applications: Suitable for applications requiring high accuracy in visual grounding tasks, where precise object localization based on textual descriptions is critical.
- Building Unified MLLMs: Can be used as a component to construct a broader multimodal LLM by merging it with other domain-specific experts, as demonstrated in the OptMerge paper.