mmE5-mllama

mmE5-mllama-11b-instruct

mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data. Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou, arXiv 2025

This model is trained based on Llama-3.2-11B-Vision.

Github

Train/Eval Data

• Train data: https://huggingface.co/datasets/intfloat/mmE5-MMEB-hardneg, https://huggingface.co/datasets/intfloat/mmE5-synthetic
• Eval data: https://huggingface.co/datasets/TIGER-Lab/MMEB-eval, https://huggingface.co/datasets/Haon-Chen/XTD-10

Experimental Results

Our model achieves SOTA performance on MMEB benchmark.

Usage

Transformers

Below is an example we adapted from VLM2Vec.

import torch
import requests

from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor

# Pooling and Normalization
def last_pooling(last_hidden_state, attention_mask, normalize=True):
    sequence_lengths = attention_mask.sum(dim=1) - 1
    batch_size = last_hidden_state.shape[0]
    reps = last_hidden_state[torch.arange(batch_size, device=last_hidden_state.device), sequence_lengths]
    if normalize:
        reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
    return reps

def compute_similarity(q_reps, p_reps):
    return torch.matmul(q_reps, p_reps.transpose(0, 1))

model_name = "intfloat/mmE5-mllama-11b-instruct"

# Load Processor and Model
processor = AutoProcessor.from_pretrained(model_name)
model = MllamaForConditionalGeneration.from_pretrained(
    model_name, torch_dtype=torch.bfloat16
).to("cuda")
model.eval()

# Image + Text -> Text
image = Image.open(requests.get('https://github.com/haon-chen/mmE5/blob/main/figures/example.jpg?raw=true', stream=True).raw)
inputs = processor(text='Represent the given image with the following question: What is in the image\n', images=[image], return_tensors="pt").to("cuda")
qry_output = last_pooling(model(**inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], inputs['attention_mask'])

string = 'A cat and a dog'
text_inputs = processor(text=string, return_tensors="pt").to("cuda")
tgt_output = last_pooling(model(**text_inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], text_inputs['attention_mask'])
print(string, '=', compute_similarity(qry_output, tgt_output))
## A cat and a dog = tensor([[0.4219]], device='cuda:0', dtype=torch.bfloat16)

string = 'A cat and a tiger'
text_inputs = processor(text=string, return_tensors="pt").to("cuda")
tgt_output = last_pooling(model(**text_inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], text_inputs['attention_mask'])
print(string, '=', compute_similarity(qry_output, tgt_output))
## A cat and a tiger = tensor([[0.3184]], device='cuda:0', dtype=torch.bfloat16)

# Text -> Image
inputs = processor(text='Find me an everyday image that matches the given caption: A cat and a dog.\n', return_tensors="pt").to("cuda")
qry_output = last_pooling(model(**inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], inputs['attention_mask'])

string = 'Represent the given image.\n'
tgt_inputs = processor(text=string, images=[image], return_tensors="pt").to("cuda")
tgt_output = last_pooling(model(**tgt_inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], tgt_inputs['attention_mask'])
print(string, '=', compute_similarity(qry_output, tgt_output))
## Represent the given image. = tensor([[0.4414]], device='cuda:0', dtype=torch.bfloat16)

inputs = processor(text='Find me an everyday image that matches the given caption: A cat and a tiger.\n', return_tensors="pt").to("cuda")
qry_output = last_pooling(model(**inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], inputs['attention_mask'])
string = 'Represent the given image.\n'
tgt_inputs = processor(text=string, images=[image], return_tensors="pt").to("cuda")
tgt_output = last_pooling(model(**tgt_inputs, return_dict=True, output_hidden_states=True).hidden_states[-1], tgt_inputs['attention_mask'])
print(string, '=', compute_similarity(qry_output, tgt_output))
## Represent the given image. = tensor([[0.3730]], device='cuda:0', dtype=torch.bfloat16)

Sentence Transformers

You can also use Sentence Transformers, where the majority of the pre- and post-processing has been abstracted.

from sentence_transformers import SentenceTransformer
import requests

# Load the model
model = SentenceTransformer("intfloat/mmE5-mllama-11b-instruct", trust_remote_code=True)

# Download an example image of a cat and a dog
dog_cat_image_bytes = requests.get('https://github.com/haon-chen/mmE5/blob/main/figures/example.jpg?raw=true', stream=True).raw.read()
with open("cat_dog_example.jpg", "wb") as f:
    f.write(dog_cat_image_bytes)

# Image + Text -> Text
image_embeddings = model.encode([{
    "image": "cat_dog_example.jpg",
    "text": "Represent the given image with the following question: What is in the image",
}])
text_embeddings = model.encode([
    {"text": "A cat and a dog"},
    {"text": "A cat and a tiger"},
])

similarity = model.similarity(image_embeddings, text_embeddings)
print(similarity)
# tensor([[0.3967, 0.3090]])
# ✅ The first text is most similar to the image

# Text -> Image
image_embeddings = model.encode([
    {"image": dog_cat_image_bytes, "text": "Represent the given image."},
])
text_embeddings = model.encode([
    {"text": "Find me an everyday image that matches the given caption: A cat and a dog."},
    {"text": "Find me an everyday image that matches the given caption: A cat and a tiger."},
])

similarity = model.similarity(image_embeddings, text_embeddings)
print(similarity)
# tensor([[0.4250, 0.3896]])
# ✅ The first text is most similar to the image

Citation

@article{chen2025mmE5,
  title={mmE5: Improving Multimodal Multili