tencent/EVIE-8B

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> 📢 Release Announcement: All model weights, inference pipelines, and evaluation suites have been fully open-sourced. Full technical details, architectural ablations, and the formal research paper will be updated in an upcoming release. Training code and distillation recipes are available in the Tencent/EVIE repository.

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🌟 Highlights

• Highest Retrieval Accuracy: 66.75 nDCG@10 on ViDoRe V3, 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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🧠 Architecture & Technical Highlights

Query Text ────────► ColQwen3.5 (BiDir Attention) ────► 4096D Per-Token Embeddings
│
MaxSim Matching
│
Doc Image ────────► ColQwen3.5 (Vision Encoder) ────► Full Multi-Vector Patch Embeddings

• Late-Interaction Token Embeddings: Rather than compressing visual documents into an information-lossy single vector, EVIE-8B preserves spatial nuances, typography, and tabular relations with per-token late-interaction representations. Relevance is scored using MaxSim:

$$ S(Q, D) = \sum_{i=1}^{|Q|} \max_{j=1}^{|D|} (q_i \cdot d_j) $$

• Full Bidirectional Attention: Employs bidirectional self-attention across multimodal vision-text sequences to facilitate cross-modal interaction between visual patch tokens and prompt contexts.
• Foundational Distillation Teacher: Serves as the high-capacity anchor model for training the compact EVIE-4.5B student via topological relation transfer and hard-negative margin supervision (ARD).

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📊 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) | | :---: | :--- | :---: | :---: | :---: | :---: | :---: | :---: | | 🥇 | [EVIE-8B](https://huggingface.co/tencent/EVIE-8B) | Qwen3.5-9B | 8.41B | 4096D | 92.18 | 74.23 | 66.75 | | 🥈 | [EVIE-4.5B](https://huggingface.co/tencent/EVIE-4.5B) | Qwen3.5-4B | 4.61B | 64–2048D Prefix-MRL | 92.07 | 73.38 | 66.02 | | 🥉 | 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 |

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🔍 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 |

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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

| Metric | ViDoRe V1 | ViDoRe V2 | ViDoRe V3 | JinaVDR | 4-Board Macro Avg | | :--- | :---: | :---: | :---: | :---: | :---: | | nDCG@1 | 88.51 | 75.58 | 62.14 | 75.50 | 75.43 | | nDCG@5 | 92.18 | 74.23 | 64.16 | 82.45 | 78.26 | | nDCG@10 | 92.64 | 75.35 | 66.75 | 83.30 | 79.51 | | Recall@1 | 88.51 | 37.87 | 30.79 | 75.50 | 58.17 | | Recall@5 | 95.07 | 66.65 | 58.69 | 88.07 | 77.12 | | Recall@10 | 96.43 | 76.34 | 70.10 | 90.65 | 83.38 | | MAP@1 | 88.51 | 75.99 | 65.94 | 75.50 | 76.49 | | MAP@5 | 91.21 | 67.30 | 55.82 | 80.56 | 73.72 | | MAP@10 | 91.40 | 66.81 | 56.32 | 80.92 | 73.86 | | MRR@1 | 88.51 | 75.99 | 65.94 | 75.50 | 76.49 | | MRR@5 | 91.21 | 82.85 | 75.39 | 80.56 | 82.50 | | MRR@10 | 91.40 | 83.23 | 76.09 | 80.92 | 82.91 |

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⚡ 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}"

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...