---
license: apache-2.0
pipeline_tag: feature-extraction
library_name: transformers
---
# 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](https://huggingface.co/papers/2510.11693), 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.
```python
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-7B") # or add a `max_pixels = 1280*28*28' for efficient encoding
model = Qwen2_5OmniThinkerForConditionalGeneration.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-7B",
torch_dtype=torch.bfloat16,
device_map="auto")
```
#### Text Batch Encodings:
```python
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:
```python
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](https://huggingface.co/blog/isaacchung/introducing-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:
```bibtex
@article{xiao2025scaling,
title={Scaling Language-Centric Omnimodal Representation Learning},
author={Xiao, Chenghao and Chan, Hou Pong and Zhang, Hao and Xu, Weiwen and Aljunied, Mahani and Rong, Yu},
journal={arXiv preprint arXiv:2510.11693},
year={2025}
}
```
