H Company Releases NeoMME: A Family of 260M and 800M Single-Tower Multimodal Encoders That Drop the Vision Tower and Causal Decoder
H Company released NeoMME, 260M/800M single-tower multimodal encoders matching 3.75B ColQwen2.5 on ViDoRe v3 while being 14.4x smaller, under Apache 2.0.
H Company released NeoMME, a family of 262,937,906- and 793,715,032-parameter bidirectional encoders that process text and raw 32x32 image patches in a single tower, pretrained via masked diffusion and released under Apache 2.0 with day-zero Hugging Face Transformers support. NeoMME-Retriever-260M reaches 0.523 nDCG@10 on ViDoRe v3, matching 3.75B-parameter ColQwen2.5 while being 14.4x smaller; the 800M model scores 0.556. Hierarchical token pooling with int8 and binary quantization shrinks late-interaction indexes from roughly 1.5 MB to 6 kB per page while retaining 95.19% of nDCG@10; text-only BEIR retrieval remains a weak spot.
- Single bidirectional tower handles text and image patches; no vision tower or decoder
- 260M retriever matches 3.75B ColQwen2.5 on ViDoRe v3 (0.523 nDCG@10)
- Masked diffusion pretraining forces cross-modal grounding; +38.4 points at 90% masking
- Pooling plus quantization cuts index size 255.5x with 95.19% quality retained
- Products
- NeoMME
- Organizations
- H Company
- AI models
- NeoMME-260MNeoMME-800MColQwen2.5
From the original article
4 min read at marktechpost.com
We look at NeoMME, a family of 260M and 800M bidirectional encoders from H Company. Unlike ColPali-style retrievers, it processes multilingual text tokens and raw 32×32 image patches in a single Transformer, with no pretrained vision tower and no causal decoder.
By Asif Razzaq · marktechpost.com
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