nomic-ai / nomic-embed-multimodal-3b

Nomic Embed Multimodal 3B: State-of-the-Art Visual Document Retrieval

nomic-embed-multimodal-3b is a dense state-of-the-art multimodal embedding model that excels at visual document retrieval tasks:

• High Performance : Achieves 58.8 NDCG@5 on Vidore-v2, outperforming all other similarly sized dense multimodal embedding models.
• Unified Text-Image Encoding : Directly encodes interleaved text and images without complex preprocessing
• Advanced Architecture : 3B parameter multimodal embedding model
• Open Weights : Model weights available for research use

Performance

Getting Started

To use nomic-embed-multimodal-3b , please install colpali from source

pip install git+https://github.com/nomic-ai/colpali.git

import torch
from PIL import Image
from transformers.utils.import_utils import is_flash_attn_2_available

from colpali_engine.models import BiQwen2_5, BiQwen2_5_Processor

model_name = "nomic-ai/nomic-embed-multimodal-3b"

model = BiQwen2_5.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="cuda:0",  # or "mps" if on Apple Silicon
    attn_implementation="flash_attention_2" if is_flash_attn_2_available() else None,
).eval()

processor = BiQwen2_5_Processor.from_pretrained(model_name)

# Your inputs
images = [
    Image.new("RGB", (128, 128), color="white"),
    Image.new("RGB", (64, 32), color="black"),
]
queries = [
    "What is the organizational structure for our R&D department?",
    "Can you provide a breakdown of last year’s financial performance?",
]

# Process the inputs
batch_images = processor.process_images(images).to(model.device)
batch_queries = processor.process_queries(queries).to(model.device)

# Forward pass
with torch.no_grad():
    image_embeddings = model(**batch_images)
    query_embeddings = model(**batch_queries)

scores = processor.score(list(torch.unbind(query_embeddings)), list(torch.unbind(image_embeddings)))

Model Architecture

• Total Parameters : 3B
• Training Approach : Fine-tuned from Qwen2.5-VL 3B Instruct
• Architecture Type : Vision-Language Model with unified text and image input processing
• Key Innovations :
  • Same-source sampling to create harder in-batch negatives
  • Hard negative mining with positive-aware techniques

Integration with RAG Workflows

Nomic Embed Multimodal 3B seamlessly integrates with Retrieval Augmented Generation (RAG) workflows:

1. Direct Document Embedding : Skip OCR and complex processing by directly embedding document page images
2. Faster Processing : Eliminate preprocessing steps for quicker indexing
3. More Complete Information : Capture both textual and visual cues in a single embedding
4. Simple Implementation : Use the same API for both text and images

Recommended Use Cases

The model excels at handling real-world document retrieval scenarios that challenge traditional text-only systems:

• Research Papers : Capture equations, diagrams, and tables
• Technical Documentation : Encode code blocks, flowcharts, and screenshots
• Product Catalogs : Represent images, specifications, and pricing tables
• Financial Reports : Embed charts, graphs, and numerical data
• Visually Rich Content : Where layout and visual information are important
• Multilingual Documents : Where visual context provides important cues

Training Details

Nomic Embed Multimodal 3B was developed through several key innovations:

1. Sampling From the Same Source : Forcing sampling from the same dataset source creates harder in-batch negatives, preventing the model from learning dataset artifacts.

2. Hard Negative Mining : Using an initial model to retrieve top-k nearest neighbors for each query, then incorporating these hard negatives into training.

3. Positive-aware Hard Negative Mining : Reducing false negatives using techniques introduced in NV-Retriever.

Limitations

• Performance may vary when processing documents with unconventional layouts or unusual visual elements
• While it handles multiple languages, performance is strongest on English content
• Processing very large or complex documents may require dividing them into smaller chunks
• Performance on documents with handwriting or heavily stylized fonts may be reduced

Join the Nomic Community

• Nomic Embed Ecosystem: https://www.nomic.ai/embed
• Website: https://nomic.ai
• Twitter: https://twitter.com/nomic_ai
• Discord: https://discord.gg/myY5YDR8z8

Citation

If you find this model useful in your research or applications, please consider citing:

@misc{faysse2024colpaliefficientdocumentretrieval,
  title={ColPali: Efficient Document Retrieval with Vision Language Models}, 
  author={Manuel Faysse and Hugues Sibille and Tony Wu and Bilel Omrani and Gautier Viaud and Céline Hudelot and Pierre Colombo},
  year={2024},
  eprint={2407.01449},
  archivePrefix={arXiv},
  primaryClass={cs.IR},
  url={https://arxiv.org/abs/2407.01449}, 
}
@misc{ma2024unifyingmultimodalretrievaldocument,
      title={Unifying Multimodal Retrieval via Document Screenshot Embedding}, 
      author={Xueguang Ma and Sheng-Chieh Lin and Minghan Li and Wenhu Chen and Jimmy Lin},
      year={2024},
      eprint={2406.11251},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2406.11251}, 
}
@misc{nomicembedmultimodal2025,
  title={Nomic Embed Multimodal: Interleaved Text, Image, and Screenshots for Visual Document Retrieval},
  author={Nomic Team},
  year={2025},
  publisher={Nomic AI},
  url={https://nomic-ai/blog/posts/nomic-embed-multimodal},
}