README.md

metadata

tags:
  - vidore
  - colpali
  - multimodal-embedding
  - multilingual-embedding
  - Text-to-Visual Document (T→VD) retrieval
  - feature-extraction
  - sentence-similarity
  - mteb
  - sentence-transformers
language:
  - multilingual
inference: false
library_name: transformers
pipeline_tag: visual-document-retrieval

The embedding model trained by Jina AI.

Jina Embeddings v4: Universal Embeddings for Multimodal Multilingual Retrieval

GGUF | Blog | Technical Report | API

Intended Usage & Model Info

jina-embeddings-v4 is a universal embedding model for multimodal and multilingual retrieval. The model is specially designed for complex document retrieval, including visually rich documents with charts, tables, and illustrations.

Built on Qwen/Qwen2.5-VL-3B-Instruct, jina-embeddings-v4 features:

• Unified embeddings for text, images, and visual documents, supporting both dense (single-vector) and late-interaction (multi-vector) retrieval.
• Multilingual support (30+ languages) and compatibility with a wide range of domains, including technical and visually complex documents.
• Task-specific adapters for retrieval, text matching, and code-related tasks, which can be selected at inference time.
• Flexible embedding size: dense embeddings are 2048 dimensions by default but can be truncated to as low as 128 with minimal performance loss.

Summary of features:

Feature	Jina Embeddings V4
Base Model	Qwen2.5-VL-3B-Instruct
Supported Tasks	retrieval, text-matching, code
Model DType	BFloat 16
Max Sequence Length	32768
Single-Vector Dimension	2048
Multi-Vector Dimension	128
Matryoshka dimensions	128, 256, 512, 1024, 2048
Pooling Strategy	Mean pooling
Attention Mechanism	FlashAttention2

Training & Evaluation

Please refer to our technical report of jina-embeddings-v4 for training details and benchmarks.

Usage

Requirements

The following Python packages are required:

• transformers>=4.52.0
• torch>=2.6.0
• peft>=0.15.2
• torchvision
• pillow

Optional / Recommended

• flash-attention: Installing flash-attention is recommended for improved inference speed and efficiency, but not mandatory.
• sentence-transformers: If you want to use the model via the sentence-transformers interface, install this package as well.

via Jina AI Embeddings API

curl https://api.jina.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $JINA_AI_API_TOKEN" \
  -d @- <<EOFEOF
  {
    "model": "jina-embeddings-v4",
    "task": "text-matching",
    "input": [
        {
            "text": "غروب جميل على الشاطئ"
        },
        {
            "text": "海滩上美丽的日落"
        },
        {
            "text": "A beautiful sunset over the beach"
        },
        {
            "text": "Un beau coucher de soleil sur la plage"
        },
        {
            "text": "Ein wunderschöner Sonnenuntergang am Strand"
        },
        {
            "text": "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία"
        },
        {
            "text": "समुद्र तट पर एक खूबसूरत सूर्यास्त"
        },
        {
            "text": "Un bellissimo tramonto sulla spiaggia"
        },
        {
            "text": "浜辺に沈む美しい夕日"
        },
        {
            "text": "해변 위로 아름다운 일몰"
        },
        {
            "image": "https://i.ibb.co/nQNGqL0/beach1.jpg"
        },
        {
            "image": "https://i.ibb.co/r5w8hG8/beach2.jpg"
        }
    ]
  }
EOFEOF

via transformers

# !pip install transformers>=4.52.0 torch>=2.6.0 peft>=0.15.2 torchvision pillow
# !pip install
from transformers import AutoModel
import torch

# Initialize the model
model = AutoModel.from_pretrained("jinaai/jina-embeddings-v4", trust_remote_code=True, torch_dtype=torch.float16)

model.to("cuda")

# ========================
# 1. Retrieval Task
# ========================
# Configure truncate_dim, max_length (for texts), max_pixels (for images), vector_type, batch_size in the encode function if needed

# Encode query
query_embeddings = model.encode_text(
    texts=["Overview of climate change impacts on coastal cities"],
    task="retrieval",
    prompt_name="query",
)

# Encode passage (text)
passage_embeddings = model.encode_text(
    texts=[
        "Climate change has led to rising sea levels, increased frequency of extreme weather events..."
    ],
    task="retrieval",
    prompt_name="passage",
)

# Encode image/document
image_embeddings = model.encode_image(
    images=["https://i.ibb.co/nQNGqL0/beach1.jpg"],
    task="retrieval",
)

# ========================
# 2. Text Matching Task
# ========================
texts = [
    "غروب جميل على الشاطئ",  # Arabic
    "海滩上美丽的日落",  # Chinese
    "Un beau coucher de soleil sur la plage",  # French
    "Ein wunderschöner Sonnenuntergang am Strand",  # German
    "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία",  # Greek
    "समुद्र तट पर एक खूबसूरत सूर्यास्त",  # Hindi
    "Un bellissimo tramonto sulla spiaggia",  # Italian
    "浜辺に沈む美しい夕日",  # Japanese
    "해변 위로 아름다운 일몰",  # Korean
]

text_embeddings = model.encode_text(texts=texts, task="text-matching")

# ========================
# 3. Code Understanding Task
# ========================

# Encode query
query_embedding = model.encode_text(
    texts=["Find a function that prints a greeting message to the console"],
    task="code",
    prompt_name="query",
)

# Encode code
code_embeddings = model.encode_text(
    texts=["def hello_world():\n    print('Hello, World!')"],
    task="code",
    prompt_name="passage",
)

# ========================
# 4. Use multivectors
# ========================

multivector_embeddings = model.encode_text(
    texts=texts,
    task="retrieval",
    prompt_name="query",
    return_multivector=True,
)

images = ["https://i.ibb.co/nQNGqL0/beach1.jpg", "https://i.ibb.co/r5w8hG8/beach2.jpg"]
multivector_image_embeddings = model.encode_image(
    images=images,
    task="retrieval",
    return_multivector=True,
)

via sentence-transformers

from sentence_transformers import SentenceTransformer

# Initialize the model
model = SentenceTransformer("jinaai/jina-embeddings-v4", trust_remote_code=True)
# ========================
# 1. Retrieval Task
# ========================
# Encode query
query_embeddings = model.encode(
    sentences=["Overview of climate change impacts on coastal cities"],
    task="retrieval",
    prompt_name="query",
)

print(f"query_embeddings.shape = {query_embeddings.shape}")

# Encode passage (text)
passage_embeddings = model.encode(
    sentences=[
        "Climate change has led to rising sea levels, increased frequency of extreme weather events..."
    ],
    task="retrieval",
    prompt_name="passage",
)

print(f"passage_embeddings.shape = {passage_embeddings.shape}")

# Encode image/document
image_embeddings = model.encode(
    sentences=["https://i.ibb.co/nQNGqL0/beach1.jpg"],
    task="retrieval",
)

print(f"image_embeddings.shape = {image_embeddings.shape}")

# ========================
# 2. Text Matching Task
# ========================
texts = [
    "غروب جميل على الشاطئ",  # Arabic
    "海滩上美丽的日落",  # Chinese
    "Un beau coucher de soleil sur la plage",  # French
    "Ein wunderschöner Sonnenuntergang am Strand",  # German
    "Ένα όμορφο ηλιοβασίλεμα πάνω από την παραλία",  # Greek
    "समुद्र तट पर एक खूबसूरत सूर्यास्त",  # Hindi
    "Un bellissimo tramonto sulla spiaggia",  # Italian
    "浜辺に沈む美しい夕日",  # Japanese
    "해변 위로 아름다운 일몰",  # Korean
]

text_embeddings = model.encode(sentences=texts, task="text-matching")

# ========================
# 3. Code Understanding Task
# ========================

# Encode query
query_embeddings = model.encode(
    sentences=["Find a function that prints a greeting message to the console"],
    task="code",
    prompt_name="query",
)

# Encode code
code_embeddings = model.encode(
    sentences=["def hello_world():\n    print('Hello, World!')"],
    task="code",
    prompt_name="passage",
)

# ========================
# 4. Use multivectors
# ========================
# If you want to use multi-vector embeddings, please use the Hugging Face model directly.

via vLLM

We provide separate model versions for each task (retrieval, text-matching, code) where specific adapter is merged into the base Qwen2.5-VL weights. This modification enables native compatibility with vLLM.

Instructions and usage examples for each task are available in their respective directories:

• jina-embeddings-v4-vllm-retrieval
• jina-embeddings-v4-vllm-text-matching
• jina-embeddings-v4-vllm-code

Please refer to the directory that matches your task for more details.

Jina-VDR

Alongside jina-embeddings-v4, we’re releasing Jina VDR, a multilingual, multi-domain benchmark for visual document retrieval. The task collection can be viewed here, and evaluation instructions can be found here.

License

This model was initially released under cc-by-nc-4.0 due to an error. The correct license is the Qwen Research License, as this model is derived from Qwen-2.5-VL-3B which is governed by that license.

Contact

Join our Discord community and chat with other community members about ideas.

Citation

If you find jina-embeddings-v4 useful in your research, please cite the following paper:

@misc{günther2025jinaembeddingsv4universalembeddingsmultimodal,
      title={jina-embeddings-v4: Universal Embeddings for Multimodal Multilingual Retrieval}, 
      author={Michael Günther and Saba Sturua and Mohammad Kalim Akram and Isabelle Mohr and Andrei Ungureanu and Sedigheh Eslami and Scott Martens and Bo Wang and Nan Wang and Han Xiao},
      year={2025},
      eprint={2506.18902},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2506.18902}, 
}