Interleaved Vision-Text Latent Reasoning (IVT-LR) is the first VLM framework that unifies textual and visual representations in the latent space and implements multimodal latent reasoning. Specifically, IVT-LR represents each reasoning step by combining two implicit parts: latent text and latent vision. We further introduce a progressive multi-stage training strategy to enable MLLMs to perform the above multimodal latent reasoning steps.
To see detailed usage, including inference code and scripts for training, please refer to the
GitHub repository.
1from huggingface_hub import hf_hub_download
2
3# Example: download Qwen2-VL model
4qwen_model_path = hf_hub_download("FYYDCC/IVTLR", "qwen_vl/model.pth")
5
6# Example: download Chameleon model
7chameleon_model_path = hf_hub_download("FYYDCC/IVTLR", "chameleon/model.pth")