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Date	Provider	Score	Summary
03 Sep 2026, 9:13 PM	Hugging Face Blog	6.5	NeoMME: an efficient Multimodal-native and Multilingual Encoder Hcompany released NeoMME, a 260M and 800M multilingual multimodal encoder that processes text tokens and raw image patches in a single bidirectional Transformer trained from scratch with masked discrete-diffusion—no separate vision tower or causal LM. Fine-tuned for visual document retrieval, the 260M model encodes ~51 pages/sec on an L40S (about 2x ColModernVBERT), and hierarchical token pooling plus asymmetric quantization cut late-interaction index storage from ~1.5 MB to 6 kB per page (255x smaller) while retaining >95% of baseline nDCG@10. Checkpoints are Apache 2.0 on Hugging Face Transformers. Why: If you ship visual document RAG, the 255x index storage reduction at >95% retained nDCG@10 is a concrete cost win—re-evaluate your ColPali/ColModernVBERT pipeline against NeoMME-Retriever, especially if you're paying for vector index storage at scale. The 2x throughput on L40S also means fewer GPUs for the same ingestion rate. multimodal-encoder visual-rag document-retrieval open-weights huggingface

Date

Provider

Score

Summary

03 Sep 2026, 9:13 PM

Hugging Face Blog

6.5

NeoMME: an efficient Multimodal-native and Multilingual Encoder

Hcompany released NeoMME, a 260M and 800M multilingual multimodal encoder that processes text tokens and raw image patches in a single bidirectional Transformer trained from scratch with masked discrete-diffusion—no separate vision tower or causal LM. Fine-tuned for visual document retrieval, the 260M model encodes ~51 pages/sec on an L40S (about 2x ColModernVBERT), and hierarchical token pooling plus asymmetric quantization cut late-interaction index storage from ~1.5 MB to 6 kB per page (255x smaller) while retaining >95% of baseline nDCG@10. Checkpoints are Apache 2.0 on Hugging Face Transformers.

Why: If you ship visual document RAG, the 255x index storage reduction at >95% retained nDCG@10 is a concrete cost win—re-evaluate your ColPali/ColModernVBERT pipeline against NeoMME-Retriever, especially if you're paying for vector index storage at scale. The 2x throughput on L40S also means fewer GPUs for the same ingestion rate.