# MLCD

## Overview The MLCD models were released by the DeepGlint-AI team in [unicom](https://github.com/deepglint/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](https://github.com/LLaVA-VL/LLaVA-NeXT) and the official training dataset [LLaVA-NeXT-Data](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Data) for evaluating the foundational visual models. - The language model is [Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct). 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)](https://huggingface.co/DeepGlint-AI/mlcd-vit-large-patch14-336)** | × | 67.84 | 76.46 | 43.48 | 531.00 | 44.30 | | **[MLCD (ViT-bigG-14-336px)](https://huggingface.co/DeepGlint-AI/mlcd-vit-bigG-patch14-336)** | √ | 71.07 | 79.63 | 44.38 | 572.00 | 46.78 | | **[MLCD (ViT-bigG-14-448px)](https://huggingface.co/DeepGlint-AI/mlcd-vit-bigG-patch14-448)** | √ | **73.80** | **83.34** | **46.59** | **582.00** | 46.00 | ## Usage ```python import requests from PIL import Image from transformers import AutoProcessor, MLCDVisionModel # Load model and processor model = MLCDVisionModel.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448") processor = AutoProcessor.from_pretrained("DeepGlint-AI/mlcd-vit-bigG-patch14-448") # Process single image url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt") # Generate outputs with torch.no_grad(): outputs = model(**inputs) # Get visual features features = outputs.last_hidden_state print(f"Extracted features shape: {features.shape}") ``` ## MLCDVisionConfig [[autodoc]] MLCDVisionConfig ## MLCDVisionModel [[autodoc]] MLCDVisionModel - forward