Add MLCD Model

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Model description

The MLCD models were released by the DeepGlint-AI team in unicom, which focuses on building foundational visual models for large multimodal language models using large-scale datasets such as LAION400M and COYO700M, and employs sample-to-cluster contrastive learning to optimize performance. MLCD models are primarily used for multimodal visual large language models, such as LLaVA.

🔥MLCD-ViT-bigG🔥 series is the state-of-the-art vision transformer model enhanced with 2D Rotary Position Embedding (RoPE2D), achieving superior performance on document understanding and visual question answering tasks. Developed by DeepGlint AI, this model demonstrates exceptional capabilities in processing complex visual-language interactions.

Tips:

• We adopted the official LLaVA-NeXT and the official training dataset LLaVA-NeXT-Data for evaluating the foundational visual models.

• The language model is Qwen2.5-7B.

Result:

Vision Tower	RoPE2D	ChartQA	DocVQA	InfoVQA	OCRBench	MMMU
CLIP (ViT-L-14-336px)	×	66.52	75.21	38.88	525.00	44.20
SigLIP (ViT-SO400M-384px)	×	69.28	76.71	41.38	554.00	46.78
DFN5B (ViT-H-14-378px)	×	64.36	70.87	38.59	473.00	48.00
MLCD (ViT-L-14-336px)	×	67.84	76.46	43.48	531.00	44.30
MLCD (ViT-bigG-14-336px)	√	71.07	79.63	44.38	572.00	46.78
MLCD (ViT-bigG-14-448px)	√	73.80	83.34	46.59	582.00	46.00

Open source status

• The model implementation is available
• The model weights are available

Provide useful links for the implementation

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