EvolvingLMMs-Lab/LLaVA-OneVision-1.5

LLaVA-OneVision-1.5 represents a significant step in the democratized LMM space, building on the highly successful LLaVA (Large Language and Vision Assistant) lineage. With 788 stars and 61 forks in ~200 days, it shows healthy adoption within the research community. Its moat is primarily its 'training recipe'—the specific combination of high-quality instruction-following data and architectural tweaks (like AnyRes and video frame sampling) that allow it to compete with much larger models. However, its defensibility is limited because it follows the 'stitching' paradigm (connecting a frozen vision encoder like SigLIP to a language model via a projector), which is increasingly being challenged by native multimodal architectures from frontier labs (e.g., GPT-4o, Gemini 1.5). The risk of displacement is high and the timeline is short (6 months) because the field moves rapidly; newer models like InternVL or Qwen2-VL often leapfrog existing benchmarks. Its primary value is as a highly customizable, open framework for labs that cannot afford the opaque APIs of frontier providers or need to fine-tune on proprietary visual data.