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 Transformer processes multilingual text tokens and raw 32×32 RGB image patches through the same layers, trained from random initialization. The retrieval fine-tune, NeoMME-Retriever, reaches 0.523 nDCG@10 on ViDoRe v3 at 260M parameters.

Is it deployable? Yes. Every checkpoint ships under Apache 2.0 with day-zero support in Hugging Face Transformers. The 260M model indexes 51.3 pages per second on a single NVIDIA L40S and encodes a query in 78.3 ms on a CPU-only host.

One tower, two modalities

Text enters through an ALBERT-style factorized embedding: a 256-dimensional lookup projected to model width. Images are split into non-overlapping 32×32 patches and projected by a 2-layer MLP trained from scratch. No patch-merging module, no SigLIP2 tower.