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🌟 Highlights

• SOTA Retrieval Performance: 66.75 nDCG@10 on ViDoRe V3 Public, delivering industry-leading visual document retrieval accuracy.
• High-Capacity 4096D Representations: Full per-token multi-vector embeddings preserving fine-grained layout, typography, charts, and table structures.
• Teacher Foundation: Provides capacity-aware relation and margin distillation targets for the lightweight EVIE-4.5B Prefix-MRL model.
• Multi-Benchmark 138-Task Coverage: Thoroughly validated across 138 tasks (ViDoRe V1, V2, V3, and JinaVDR) across 4 standard metric families (nDCG, Recall, MAP, MRR @1/5/10).

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📊 Comprehensive ViDoRe Leaderboard Comparison

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

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🔍 ViDoRe V3 Per-Domain Breakdown (nDCG@10)

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📋 Complete 138-Task Evaluation Matrix

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

• ViDoRe V1: 10 tasks, 3,943 queries, 6,729 docs
• ViDoRe V2: 4 tasks, 1,152 queries, 4,544 docs
• ViDoRe V3: 48 tasks, 14,514 queries, 115,512 docs
• JinaVDR: 76 tasks, 43,375 queries, 55,398 docs

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⚡ Quick Start

Installation

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

Python Inference

import torch
from PIL import Image
from colpali_engine.models import ColQwen3_5, ColQwen3_5Processor

model_id = "tencent/EVIE-8B"

# 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. 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)

# 3. Generate 4096D multi-vectors 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)

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📂 Repository Layout

Evie-8B/
├── model.safetensors         # EVIE-8B weights (safetensors)
├── config.json               # Model configuration (4096D)
├── infer.py                  # Standalone inference & scoring CLI
├── colpali/                  # ColQwen3.5 inference engine
├── code/shared/              # 138-task eval harness
├── examples/demo/            # 8-page retrieval demo (run.sh)
└── env.sh.example            # Environment variables template

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🔬 Released Checkpoint

EVIE-8B was trained on 775,635 document-query pairs across multilingual domains. Hard negatives were mined and classified into answerable (promoted to positives), ambiguous (masked), and strict negatives (negative cross-entropy loss).

The released a40 checkpoint represents a weight-space mix ($\alpha = 0.40$) of two independently trained arms, achieving the peak 66.75 nDCG@10 on ViDoRe V3. Full training reproduction scripts are provided in the companion repository Tencent/EVIE.

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📚 Citation

@misc{tencent2026evie8b,
  title        = {EVIE-8B: High-Capacity Visual Document Retrieval with 4096-Dimensional Embeddings},
  author       = {{Tencent}},
  year         = {2026},
  howpublished = {\url{https://github.com/Tencent/EVIE}}
}

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📄 License

This model and codebase are released under the Apache-2.0 License.