--- datasets: - starriver030515/FUSION-Pretrain-10M - starriver030515/FUSION-Finetune-12M base_model: - microsoft/Phi-3.5-mini-instruct - google/siglip-so400m-patch14-384 license: apache-2.0 --- # Model Card for FUSION This is the checkpoint after Stage 1 training of FUSION-Phi3.5-3B. ## Model Details **Model Description** encoder decoder FUSION is a family of multimodal large language models that adopts a fully integrated vision-language architecture, enabling comprehensive and fine-grained cross-modal understanding. In contrast to prior approaches that primarily perform shallow or late-stage modality fusion during the LLM decoding phase, FUSION achieves deep, dynamic integration across the entire vision-language processing pipeline. To enable this, FUSION utilizes Text-Guided Unified Vision Encoding, which incorporates textual context directly into the vision encoder. This design allows for pixel-level vision-language alignment and facilitates early-stage cross-modal interaction. During decoding, FUSION employs Context-Aware Recursive Alignment Decoding strategy. This component dynamically aggregates and refines visual features based on the evolving textual context at each decoding step, allowing the model to capture question-level semantics with high precision. To further enhance alignment and reduce the semantic gap between modalities, FUSION integrates Dual-Supervised Semantic Mapping Loss, which provides simultaneous supervision in both visual and textual embedding spaces. This dual-path guidance strengthens the consistency and semantic coherence of the fused representations. **Base Model** **LLM**: [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) **Vision Encoder**: [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) ## Training Details **Training Strategies** FUSION is trained with a three-stage training framework, ensuring comprehensive alignment and integration between visual and linguistic modalities. - **Stage1: Foundational Semantic Alignment**: We pretrain the vision encoder using extensive image-caption datasets to establish precise semantic alignment be- tween visual and textual representations. - **Stage1.5: Contextual Multimodal Fusion**: In contrast to Stage 1, this intermediate stage incorporates various types of QA data along with image-caption pairs. This phase is designed to enhance the model’s adaptability in aligning vision and language representations across a broad spectrum of scenarios. - **Stage2: Visual Instruction Tuning**: At this stage, we expose the model to various visual tasks, enabling it to answer downstream vision-related questions effectively. **Training Data** - [10M FUSION Alignment Data](https://huggingface.co/datasets/starriver030515/FUSION-Pretrain-10M) For Stage1 - [12M FUSION Curated Instruction Tuning Data](https://huggingface.co/datasets/starriver030515/FUSION-Finetune-12M) For Stage1.5 and Stage2 ## Performance performance **Where to send questions or comments about the model:** https://github.com/starriver030515/FUSION/issues ## Paper or resources for more information - https://github.com/starriver030515/FUSION - Coming soon~