H Company Releases NeoMME: A Family of 260M and 800M Single-Tower Multimodal Encoders That Drop the Vision Tower and Causal Decoder
Most visual document retrievers in production today are hand-me-downs. ColPali and the models that followed it take a generative vision-language model and repurpose it as an encoder. The result still carries a separately pretrained vision tower and a causal decoder that never generates a token. That is parameter and compute overhead for a task that only needs representations. H Company has released NeoMME, a family of 260M and 800M bidirectional encoders that drops both components. One Trans
H Company has released NeoMME, a family of multimodal encoders in 260M and 800M parameter sizes designed specifically for visual document retrieval. Unlike existing document retrievers that repurpose generative vision-language models, NeoMME uses a single bidirectional Transformer that processes both text tokens and raw image patches through the same layers, eliminating the separate vision tower and decoder that previous systems carried as computational overhead. The 260M model achieves competitive performance compared to much larger models while being deployable at practical speeds, indexing pages at 51.3 pages per second on a single GPU. The models are released under Apache 2.0 with support in Hugging Face Transformers, and token pooling techniques can reduce index storage from roughly 1.5 MB to 6 kB per page while retaining most retrieval accuracy.
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