LCO-Embedding/LCO-Embedding-Omni-3B

LCO-Embedding: Scaling Language-Centric Omnimodal Representation Learning

We are thrilled to release LCO-Embedding - a language-centric omnimodal representation learning framework and the LCO-Embedding model families!

This model implements the framework presented in the paper Scaling Language-Centric Omnimodal Representation Learning, accepted by NeurIPS 2025.

Project Page: https://huggingface.co/LCO-Embedding

Github Repository: https://github.com/LCO-Embedding/LCO-Embedding

Quick Start

Note: We are only using the thinker component of Qwen2.5 Omni and drops the talker component.

from transformers import Qwen2_5OmniThinkerForConditionalGeneration, Qwen2_5OmniProcessor
from qwen_omni_utils import process_mm_info

processor = Qwen2_5OmniProcessor.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-3B") # or add a `max_pixels = 1280*28*28' for efficient encoding
model = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-3B",
                                                                    torch_dtype=torch.bfloat16,
                                                                    device_map="auto")

Text Batch Encodings:

texts = ["some random text", "a second random text", "a third random text"] * 30
batch_size = 8
text_prompt =  "{}\nSummarize the above text in one word:" 

all_text_embeddings = []

with torch.no_grad():
    for i in tqdm(range(0, len(texts), batch_size)):
        batch_texts = texts[i : i + batch_size]
        batch_texts = [text_prompt.format(text) for text in batch_texts]
        messages = [[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text":text},
                ],

            }
        ] for text in batch_texts]
        text_inputs = processor.apply_chat_template(messages, tokenize = False, add_generation_prompt = True)
        text_inputs = processor(
        text = text_inputs,
        padding = True,
        return_tensors = "pt",
        )
        text_inputs = text_inputs.to("cuda")
        text_outputs = model(
            **text_inputs, output_hidden_states=True, return_dict=True
        ).hidden_states[-1][:, -1, :]
        all_text_embeddings.append(text_outputs.to(torch.float16).cpu())

all_text_embeddings = torch.cat(all_text_embeddings, dim=0)

Image Batch Encodings:

images = [some random PIL.Image] * 100 # will be good to load them using dataloader; see MIEB evaluation pipeline
image_prompt = "\nSummarize the above image in one word:"
batch_size = 8

all_image_embeddings = []

with torch.no_grad():
    for i in tqdm(range(0, len(images), batch_size)):
        batch_images = images[i : i + batch_size]
        messages = [[
            {
                "role": "user",
                "content": [
                    {"type": "image", "image":image},
                    {"type": "text", "text": image_prompt},
                ],

            }
        ] for image in batch_images]
        text = processor.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True
        )
        audio_inputs, image_inputs, video_inputs = process_mm_info(messages, use_audio_in_video=True)
        inputs = processor(
            text=text, 
            audio=audio_inputs, 
            images=image_inputs, 
            videos=video_inputs, 
            return_tensors="pt", 
            padding=True
        )
        inputs = inputs.to("cuda")
        image_outputs = model(
            **inputs, output_hidden_states=True, return_dict=True
        ).hidden_states[-1][:, -1, :]
        all_image_embeddings.append(image_outputs.to(torch.float16).cpu())

all_image_embeddings = torch.cat(all_image_embeddings, dim=0)

Overview

We introduce LCO-Embedding, a language-centric omnimodal representation learning method and the LCO-Embedding model families, setting a new state-of-the-art on MIEB (Massive Image Embedding Benchmark), while supporting audio and videos.

This work also introduces the Generation-Representation Scaling Law, connecting models' generative capabilities and their representation upper bound. Furthermore, we introduce SeaDoc, a challenging visual document retrieval task in Southeast Asian languages, and show that continual generative pretraining before contrastive learning raises the representation upper bound.

Evaluation Results

We evaluate LCO-Embedding with state-of-the-art embedding models, including E5-V, Voyage Multimodal 3, mmE5, and GME, on a MIEB-Lite benchmark (51 tasks) broken down by task categories.

Performance and efficiency comparisons of different training strategies using 3B and 7B variants of Qwen2.5-VL backbones.

Scaling relationship between generation benchmark performance (X-axis) and representation benchmark performance after language-centric contrastive learning (Y-axis).

Citation

If you find LCO-Embedding useful for your research and applications, please cite using this BibTeX:

@misc{xiao2025scaling,
    title={Scaling Language-Centric Omnimodal Representation Learning}, 
    author={Chenghao Xiao and Hou Pong Chan and Hao Zhang and Weiwen Xu and Mahani Aljunied and Yu Rong},
    year={2025},
    eprint={2510.11693},
    archivePrefix={arXiv},
    primaryClass={cs.CL},
    url={https://arxiv.org/abs/2510.11693}, 
}