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Mulberry Qwen2vl 7b
M
Mulberry Qwen2vl 7b
Developed by HuanjinYao
The Mulberry model is a step-by-step reasoning-based model trained on the Mulberry - 260K SFT dataset generated through collective knowledge search.
Downloads 13.57k
Release Time : 2/4/2025
Model Overview
The Mulberry model is a multimodal model based on Qwen2-VL-7B-Instruct, focusing on step-by-step reasoning tasks and suitable for application scenarios requiring complex reasoning and multimodal understanding.
Model Features
Step-by-step reasoning ability
The model processes complex tasks through step-by-step reasoning, capable of decomposing problems and solving them step by step.
Multimodal understanding
Based on the Qwen2-VL architecture, it supports multimodal input and understanding of vision and language.
Collective knowledge search
Using the training data generated by the CoMCTS method, it enhances the model's collective knowledge integration ability.
Model Capabilities
Multimodal reasoning
Visual language understanding
Complex problem decomposition
Step-by-step task solving
Use Cases
Education
Multimodal teaching assistance
Helps students understand complex concepts combining images and text.
Improves learning efficiency and depth of understanding
Research
Scientific problem reasoning
Assists researchers in decomposing and solving complex scientific problems.
Accelerates the research process
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