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EVIE-4.5B

Visual Document Retrieval
Safetensors
sentence-transformers
colpali-engine
qwen3_5
vision-language
colbert
late-interaction
multi-vector
matryoshka
vidore
token-compression
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πŸ† EVIE: The Most Accurate and Lightweight Visual Document Retriever

Evidence-Vector-Informed Embedding (EVIE)

ViDoRe Leaderboard SOTA Papers with Code SOTA

License GitHub Hugging Face 4.5B Hugging Face 8B Backbone Storage

High-Precision Late-Interaction Retrieval β€’ Dynamic Prefix-MRL (64D–2048D) β€’ Training-Free HAC Token Compression

πŸ€— EVIE-4.5B (Prefix-MRL & HAC)  β€’  πŸ€— EVIE-8B (Flagship Teacher)  β€’  πŸ™ GitHub: Tencent/EVIE

πŸ“’ Release Announcement: All model weights, training pipelines, token compression algorithms (HAC), and evaluation suites have been fully open-sourced in this repository. Full technical details, architectural ablations, and the formal research paper will be updated in an upcoming release.

🌟 Highlights

  • Top-Tier Benchmark Performance: 66.75 on ViDoRe V3 for EVIE-8B and 66.02 for EVIE-4.5B with single-projection Prefix-MRL.
  • ⚑ Prefix-MRL Elasticity: Single 2048D linear projection. Freely truncate at runtime into ${64, 128, 256, 512, 1024, 2048}$ dimensions without separate models.
  • πŸ“¦ Ultra-Compact Index (HAC): Training-free Hierarchical Agglomerative Clustering compresses token counts from ~750 down to 32 vectors/page, slashing index storage to 3.81 GiB per million pages.
  • 🌐 138 Multilingual Tasks Evaluated: Thoroughly evaluated across ViDoRe V1, V2, V3, and JinaVDR across 4 metric families (nDCG, Recall, MAP, MRR @1/5/10).
  • πŸ”¬ ARD Distillation Recipe: Anchor-preserving, capacity-aware relation distillation reproducing full student training from the 8B teacher.

🧠 Architecture & Technical Highlights 

 Query Text  ────────► ColQwen3.5 (BiDir Attention) ────► Elastic Multi-Vectors (64D–2048D)
                                                                    β”‚
                                                           MaxSim Matching
                                                                    β”‚
 Doc Image   ────────► ColQwen3.5 (Vision Encoder)  ────► HAC Compression ──► 32 Vectors / Page
  • Late-Interaction Multi-Vector Paradigm: Unlike dense single-vector retrieval that collapses high-resolution document pages into a single point, EVIE preserves fine-grained visual details (complex tables, layout structures, charts, and small typography) through token-level representations, scoring relevance via late-interaction MaxSim:

S(Q,D)=βˆ‘i=1∣Q∣max⁑j=1∣D∣(qiβ‹…dj) S(Q, D) = \sum_{i=1}^{|Q|} \max_{j=1}^{|D|} (q_i \cdot d_j)

  • Prefix-MRL (Single-Head Elastic Representation): EVIE-4.5B introduces single-projection Prefix-MRL. A single 2048D linear projection natively supports runtime truncation down to {64, 128, 256, 512, 1024, 2048} dimensions without maintaining multiple heads or separate checkpoints.
  • ARD (Anchor-preserving Relation Distillation): The 4.5B student is distilled from the 8B teacher using token-relation topological geometry, hard-negative margin calibration, and anchor-preserving alignment, maintaining peak retrieval accuracy even under low-dimensional prefixes.
  • HAC Token Compression (Hierarchical Agglomerative Clustering): A plug-and-play, training-free token reduction algorithm that aggregates visual patch tokens into 32 or 64 semantic centroids in joint feature-spatial space, reducing 1M-page index footprints to as little as 3.81 GiB.

πŸ† ViDoRe V3 Leaderboard

Official ranking on the ViDoRe leaderboard (ViDoRe V3, Mean Task).

Rank Model Parameters Mean (Task)
πŸ₯‡ #1 tencent/EVIE-8B 8.4B 66.24
πŸ₯ˆ #2 tencent/EVIE-4.5B 4.5B 65.70
#3 webAI-ColVec1.1-8b 8.4B 64.95
#4 VultronRetrieverPrime-Qwen3.5-8B 8.4B 64.26
#5 webAI-ColVec1.1-4b 4.5B 63.90
#6 VultronRetrieverCore-Qwen3.5-4.5B 4.5B 63.57

πŸ“Š Comprehensive ViDoRe Leaderboard Comparison

Performance comparison across modern multi-vector late-interaction visual document retrievers on ViDoRe:

Rank Model Base Model Param Embed Dim ViDoRe V1 (nDCG@5) ViDoRe V2 (nDCG@5) ViDoRe V3 (nDCG@10)
πŸ₯‡ EVIE-8B Qwen3.5-9B 8.41B 4096D 92.18 74.23 66.75
πŸ₯ˆ EVIE-4.5B Qwen3.5-4B 4.61B 64–2048D Prefix-MRL 92.07 73.38 66.02
πŸ₯‰ EVIE-Preview-4.5B Qwen3.5-4B 4.54B 128D 91.73 70.87 65.36
4 webAI-ColVec1.1-8b Qwen2.5-VL 8.40B 640D 91.30 65.82 65.32
5 VultronRetrieverPrime-8B Qwen3.5-9B 8.40B 320D 92.08 68.18 64.26
6 webAI-ColVec1.1-4b Qwen2.5-VL 4.54B 640D 90.49 63.60 63.90
7 VultronRetrieverCore-4.5B Qwen3.5-4B 4.50B 320D 92.21 66.12 63.57
8 nemotron-colembed-vl-8b-v2 Nemotron-8B 8.80B 4096D 92.65 65.16 63.54
9 tomoro-colqwen3-embed-8b Qwen2.5-VL 8.00B 320D 90.76 65.40 61.60
10 nemotron-colembed-vl-4b-v2 Nemotron-4B 4.80B 2560D 91.62 64.49 61.42
11 athrael-soju/colqwen3.5-4.5B-v3 Qwen3.5-4B 4.60B 128D 91.54 64.25 61.46
12 tomoro-colqwen3-embed-4b Qwen2.5-VL 4.00B 320D 90.57 64.69 60.16
13 VultronRetrieverFlash-0.8B Qwen3.5-0.8B 0.85B 320D 88.15 60.36 56.16

πŸ” ViDoRe V3 Per-Domain Breakdown (nDCG@10)

Model Avg CompSci Energy Finance EN Finance FR HR Industrial Pharma Physics
EVIE-8B 66.75 81.86 72.51 71.23 56.40 69.29 59.77 70.81 52.11
EVIE-4.5B 66.02 81.72 72.32 70.00 54.90 67.82 59.40 70.27 51.69
webAI-ColVec1.1-8b 65.32 80.08 70.12 71.90 54.87 68.55 57.65 67.88 51.50
nemotron-colembed-vl-8b-v2 63.54 79.30 69.82 67.29 51.54 66.32 56.03 67.19 50.84
VultronRetrieverPrime-8B 64.26 79.80 70.30 69.00 54.50 66.80 57.40 68.20 51.70
VultronRetrieverCore-4.5B 63.57 79.80 69.20 68.90 52.00 66.10 56.10 67.50 50.20
tomoro-colqwen3-embed-8b 61.60 75.35 68.41 65.08 49.10 63.98 54.41 66.36 50.13

🎯 Prefix-MRL Elastic Multi-Vector Head

EVIE-4.5B embeds document and query tokens with a single 2048D linear projection head trained via ARD. You can truncate the channel dimension on-the-fly without maintaining different models:

Full Projection (2048D)  [========================================================] 66.02
Prefix 1024D             [============================]                             65.94
Prefix 512D              [==============]                                           65.90
Prefix 256D              [=======]                                                  65.68
Prefix 128D              [===]                                                      65.27
Prefix 64D               [=]                                                        64.51
Dimension Bytes / Vector ViDoRe V1 ViDoRe V2 ViDoRe V3 JinaVDR 138-Task Avg4
64 128 B 92.16 73.18 64.51 81.00 77.71
128 256 B 92.28 73.37 65.27 81.83 78.18
256 512 B 92.20 73.83 65.68 82.09 78.45
512 1 KiB 92.38 74.53 65.90 82.27 78.77
1024 2 KiB 92.39 74.53 65.94 82.43 78.82
2048 4 KiB 92.53 74.91 66.02 82.48 78.98

πŸ—œοΈ Token Compression (HAC)

Raw late-interaction representations keep all visual patch vectors (~750 vectors/page), requiring substantial storage. EVIE integrates Hierarchical Agglomerative Clustering (HAC) in a joint semantic-position space to cluster page tokens at indexing time:

z=L2[(1βˆ’w)L2(v)+wp],ΞΌc=L2(meani∈cvi) z = \text{L2}\left[(1-w)\text{L2}(v) + w p\right], \quad \mu_c = \text{L2}\left(\text{mean}_{i \in c} v_i\right)

Production Ready SKUs

SKU Payload / Page Index Size (1M Pages) Vectors / Page ViDoRe V1 ViDoRe V2 ViDoRe V3 Avg4
d64 K32 4 KiB 3.81 GiB 32 89.27 67.83 59.58 73.47
d64 K64 8 KiB 7.63 GiB 64 90.63 70.71 62.06 75.57
d128 K32 8 KiB 7.63 GiB 32 90.00 69.45 61.40 74.87

πŸ“‹ Complete 138-Task Evaluation Matrix

Protocol paired-all-pages-dedup+process_queries+ndcg2r-20260827 ($\text{MVT} = 1024$, bidirectional attention):

Metric ViDoRe V1 ViDoRe V2 ViDoRe V3 JinaVDR 4-Board Macro Avg
nDCG@1 88.35 72.76 61.16 74.26 74.13
nDCG@5 92.07 73.38 63.39 81.57 77.60
nDCG@10 92.53 74.91 66.02 82.48 78.98
Recall@1 88.35 36.73 30.14 74.26 57.37
Recall@5 94.95 65.59 58.06 87.39 76.50
Recall@10 96.36 76.56 69.67 90.17 83.19
MAP@1 88.35 73.18 65.03 74.26 75.20
MAP@5 91.09 66.52 55.02 79.61 73.06
MAP@10 91.29 66.25 55.50 79.99 73.25
MRR@1 88.35 73.18 65.03 74.26 75.20
MRR@5 91.09 81.66 74.76 79.61 81.78
MRR@10 91.29 82.11 75.43 79.99 82.20

⚑ Quick Start

ColPali Engine

Installation 

git clone https://github.com/Tencent/EVIE.git
cd EVIE
pip install -r requirements.txt
export PYTHONPATH="$(pwd)/colpali${PYTHONPATH:+:$PYTHONPATH}"

Self-Contained Python Inference 

import torch
from PIL import Image
from colpali_engine.models import ColQwen3_5, ColQwen3_5Processor
from colpali_engine.models.qwen3_5.colqwen3_5.modeling_colqwen3_5 import set_active_head

model_id = "tencent/EVIE-4.5B"

# 1. Load model with FlashAttention and bidirectional attention
model = ColQwen3_5.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="cuda",
    attn_implementation="flash_attention_2",
).eval()
model.enable_bidirectional_attention()

# 2. Select any Matryoshka head: 64, 128, 256, 512, 1024, or 2048
set_active_head(model, 128)

# 3. Process query and document image
processor = ColQwen3_5Processor.from_pretrained(model_id)
images = [Image.open("examples/demo/pages/q3_revenue.png").convert("RGB")]
queries = ["What is the total quarterly revenue?"]

image_batch = processor.process_images(images).to(model.device)
query_batch = processor.process_queries(queries).to(model.device)

# 4. Generate multi-vector representations and late-interaction score
with torch.inference_mode():
    image_embeddings = model(**image_batch)
    model.rope_deltas = None  # Reset RoPE deltas before text query forward
    query_embeddings = model(**query_batch)

scores = processor.score(query_embeddings, image_embeddings)
print("Late-interaction MaxSim Relevance Score:", scores)

Sentence Transformers 

pip install "sentence-transformers[image] @ git+https://github.com/huggingface/sentence-transformers.git"

from sentence_transformers import MultiVectorEncoder

model = MultiVectorEncoder("tencent/EVIE-4.5B")

queries = [
    "What is the variable represented on the y-axis of the graph?",
    "Total outlay is maximum in which year?",
]
documents = [
    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/doc1.jpg",
    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/doc2.jpg",
    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/doc3.jpg",
    "https://huggingface.co/datasets/sentence-transformers/example-documents/resolve/main/doc4.jpg",
]

query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings[0].shape, document_embeddings[0].shape)
# torch.Size([23, 2048]) torch.Size([991, 2048])

scores = model.similarity(query_embeddings, document_embeddings)
print(scores)
# tensor([[17.2500,  7.6084,  6.7920,  4.8398],
#         [ 4.5312, 12.7812,  4.3530,  4.2368]])

Documents can be URLs, local image paths, or PIL.Image objects. The example returns the full 2048-dimensional token embeddings. Scores can vary slightly with dtype and attention backend. To use a smaller Prefix-MRL dimension, slice and renormalize:

from torch.nn.functional import normalize

dimension = 128
query_embeddings = [normalize(embedding[..., :dimension], dim=-1) for embedding in query_embeddings]
document_embeddings = [normalize(embedding[..., :dimension], dim=-1) for embedding in document_embeddings]
scores = model.similarity(query_embeddings, document_embeddings)

πŸ“‚ Repository Layout 

EVIE/
β”œβ”€β”€ model.safetensors         # EVIE-4.5B weights (safetensors)
β”œβ”€β”€ config.json               # Model configuration (Prefix-MRL, max 2048)
β”œβ”€β”€ processor_config.json     # Multimodal processor config
β”œβ”€β”€ infer.py                  # Standalone inference & scoring CLI
β”œβ”€β”€ colpali/                  # ColQwen3.5 + Prefix-MRL + ARD
β”œβ”€β”€ code/
β”‚   β”œβ”€β”€ teacher/              # EVIE-8B training arms and soup merging
β”‚   β”œβ”€β”€ student/              # EVIE-4.5B Prefix-MRL / ARD distillation
β”‚   β”œβ”€β”€ shared/               # Data loaders, adapter merges, and 138-task eval harness
β”‚   └── compress/             # Training-free HAC token compression pipeline
β”œβ”€β”€ examples/demo/            # 8-page retrieval demo (run.sh)
└── env.sh.example            # Environment variables template

πŸ”¬ Training & Distillation Reproduction

Student training executes Prefix-MRL plus ARD: capacity-aware relation distillation and margin distillation against frozen EVIE-8B, with a Preview-anchor term on the 128D prefix:

cp env.sh.example env.sh
source env.sh

# Step 1: Train EVIE-8B teacher arms and merge
bash code/teacher/run.sh

# Step 2: Distill EVIE-4.5B student with ARD loss
export TEACHER_DIR=../Evie-8B
bash code/student/run.sh

# Step 3: Run comprehensive 138-task evaluation
MODEL_DIR=. RUN_NAME=evie-4.5b bash code/shared/eval_run.sh

πŸ“š Citation 

@misc{tencent2026evie,
  title        = {EVIE: High-Performance Multilingual Visual Document Retrieval with Matryoshka Embeddings and Token Compression},
  author       = {Wang, Zifei and Wen, Wei},
  year         = {2026},
  howpublished = {\url{https://github.com/Tencent/EVIE}},
  note         = {Corresponding author: Wei Wen <jawnrwen@tencent.com>}
}

πŸ“„ License

This repository is licensed under the Apache-2.0 License.

πŸ‘₯ Authors

Name Role Affiliation Contact
Zifei Wang Author Tencent IMA Product Center zifeiwang@tencent.com
Wei Wen Author & Corresponding Tencent Youtu Lab jawnrwen@tencent.com
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