# NeoMME: an efficient Multimodal-native and Multilingual Encoder

DevFeed: [NeoMME: an efficient Multimodal-native and Multilingual Encoder](<https://devfeed.tech/articles/neomme-an-efficient-multimodal-native-and-multilingual-encoder-7011.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/Hcompany/neomme>)

Author: Tony Wu; Aurélien Lac

Published: 2026-09-03T13:13:48Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [vlm](<https://devfeed.tech/topics/vlm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [GPU](<https://devfeed.tech/topics/gpu.md>)

Tags: [apache](<https://devfeed.tech/tags/apache.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [diffusion](<https://devfeed.tech/tags/diffusion.md>), [embeddings](<https://devfeed.tech/tags/embeddings.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [multimodal](<https://devfeed.tech/tags/multimodal.md>), [quantization](<https://devfeed.tech/tags/quantization.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [training](<https://devfeed.tech/tags/training.md>), [transformers](<https://devfeed.tech/tags/transformers.md>), [vector](<https://devfeed.tech/tags/vector.md>), [vision](<https://devfeed.tech/tags/vision.md>), [vlm](<https://devfeed.tech/tags/vlm.md>)

## AI overview

NeoMME is a family of multilingual multimodal encoders trained from scratch with a masked discrete-diffusion objective. It uses one bidirectional Transformer for text tokens and image patches, and is fine-tuned for visual document retrieval with dense and late-interaction embeddings.

## Source excerpt

We introduce NeoMME, a family of 260M and 800M multilingual multimodal encoders. Unlike many generative visual language models, NeoMME does not use a separate pretrained vision tower or a causal language model. A single bidirectional Transformer processes both text tokens and raw image patches, and we train the entire model from scratch with a masked discrete-diffusion objective. We fine-tuned NeoMME for visual document retrieval using ColPali's page-image approach.