tencent/EVIE-Preview-4.5B · Hugging Face

Source: https://huggingface.co/tencent/EVIE-Preview-4.5B

https://huggingface.co/tencent/EVIE-Preview-4.5B#overviewOverview

EVIE-Preview-4.5Bis a state-of-the-art multilingual Visual Document Retrieval (VDR) model built uponQwen3.5-4B. It employs ColBERT-style late interaction with native128-dimensionalmulti-vector token embeddings (4.54B parameters, BF16).

By combining native GatedDeltaNet linear-attention and full-attention hybrid modeling with a compact visual projection, EVIE achieves top-tier performance across ViDoRe V1+V2 and ViDoRe V3 while generating compact 128D multi-vector representations.

Note: This is a preview release. The next iteration of EVIE is pending release.

https://huggingface.co/tencent/EVIE-Preview-4.5B#key-highlightsKey Highlights

• 🎯 Ultra-Compact 128D Token Vectors: Native 128-dimensional multi-vector representations keep each token vector narrow while preserving strong retrieval quality.
• 🏆 SOTA on ViDoRe Benchmarks: Outperforms larger 8B models on ViDoRe V3 (leading7 of 8public domains) and delivers top average accuracy on ViDoRe V1+V2 (85.93nDCG@5).
• 🌐 Robust Multilingual & Multi-Format: Strong zero-shot generalization across diverse languages (EN, FR, DE, IT, ES, PT, ZH, etc.) and visual formats (charts, tables, scientific reports, financial filings).
• ⚡ Seamless ColPali Compatibility: Fully integrated with the standardcolpali\-engineecosystem and late-interaction scoring pipelines.

https://huggingface.co/tencent/EVIE-Preview-4.5B#architectureArchitecture

Text Query ───────► ColQwen3_5 (BiDir Attn) ─────► Query Token Embeddings (128D)
                                                                 │
                                                       Late Interaction (MaxSim) ──► Relevance Score
                                                                 │
Document Image ─────► ColQwen3_5 (Dynamic Vision) ──► Doc Token Embeddings (128D)

1. Vision-Language Backbone: Built onQwen3\.5\-4Bwith interleaved linear and full attention layers.
2. Compact Projection: Projects contextual token states directly into 128-dimensional representations.
3. Late-Interaction Retrieval: Calculates similarity via token-level MaxSim operator across query tokens and document visual tokens.

https://huggingface.co/tencent/EVIE-Preview-4.5B#benchmark-resultsBenchmark Results

https://huggingface.co/tencent/EVIE-Preview-4.5B#model-footprintModel Footprint

Relative width compares the native uncompressed width of one token vector with EVIE’s 128D output. It is not a total index-size comparison: total storage also depends on vectors per page, data precision, projection or compression settings, and index overhead.

https://huggingface.co/tencent/EVIE-Preview-4.5B#vidore-v3-8-public-domains-ndcg10ViDoRe V3: 8 Public Domains (nDCG@10)

Evaluated across 8 domains with queries spanning 6 languages (EN, FR, DE, IT, PT, ES):

ModelAvgCompSciEnergyFinance (EN)Finance (FR)HRIndustrialPharmaPhysicsEVIE-Preview-4.5B64.4080.3371.4567.68****53.0165.6557.4868.6650.96nemotron-colembed-vl-8b-v263.5479.3069.8267.2951.5466.3256.0367.1950.84tomoro-colqwen3-embed-8b61.6075.3568.4165.0849.1063.9854.4166.3650.13nemotron-colembed-vl-4b-v261.4278.5667.4865.0249.0162.3953.9166.1048.86tomoro-colqwen3-embed-4b60.1675.4466.4363.8446.8360.0953.5865.7449.32llama-nemotron-colembed-vl-3b-v259.7077.0964.8864.2344.4162.2851.7166.0446.93colnomic-embed-multimodal-7b57.6476.2063.5856.5745.4658.6750.1362.2648.25jina-embeddings-v457.5471.8163.5059.3046.1059.5350.3863.0946.63

🏆Result: EVIE leads in7 out of 8public domains on ViDoRe V3.

https://huggingface.co/tencent/EVIE-Preview-4.5B#vidore-v1–v2-ndcg5ViDoRe V1 + V2 (nDCG@5)

ModelAvgArxivQADocVQAInfoVQAShiftProjSynAISynEnergySynGovSynHealthTabfquadTatdqaBioMedESGHLESGEconEVIE-Preview-4.5B****85.991.562.993.094.0100.099.0****98.998.997.581.671.080.166.3****68.3Ops-Colqwen3-4B84.991.866.594.090.899.697.398.099.693.682.465.578.666.064.5nemotron-colembed-vl-8b-v284.893.168.194.693.3100.097.998.999.697.783.466.273.260.660.8nemotron-colembed-vl-4b-v283.992.067.493.392.399.396.298.098.598.181.264.371.461.560.8colqwen3.5-4.5B-v383.791.966.693.690.2100.097.197.398.995.984.065.373.858.059.9llama-nemotron-colembed-vl-3b-v283.690.467.294.792.0100.098.098.098.997.381.063.273.158.658.6tomoro-colqwen3-embed-8b83.591.266.494.587.999.396.797.699.194.280.965.576.060.759.5EvoQwen2.5-VL-Retriever-7B-v183.491.565.194.188.899.696.696.398.993.682.365.277.059.759.1tomoro-colqwen3-embed-4b83.290.666.394.387.499.396.997.299.694.379.965.474.662.456.3llama-nemoretriever-colembed-3b-v183.188.466.294.990.799.696.697.899.395.980.662.775.457.457.8SauerkrautLM-ColQwen3-8b-v0.182.993.864.794.590.498.696.596.899.392.284.063.370.857.958.0 Avg: Unweighted mean across all 14 tasks. Tasks 1–10: ViDoRe V1. Tasks 11–14: ViDoRe V2.

https://huggingface.co/tencent/EVIE-Preview-4.5B#quick-startQuick Start

https://huggingface.co/tencent/EVIE-Preview-4.5B#installationInstallation

pip install -r requirements.txt

https://huggingface.co/tencent/EVIE-Preview-4.5B#python-inferencePython Inference

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

# Use the Hugging Face model repository or local directory
model_id = "tencent/EVIE-Preview-4.5B"

# 1. Load model and enable 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. Load processor
processor = ColQwen3_5Processor.from_pretrained(model_id)

# 3. Prepare inputs
images = [Image.open("document_page.png")]
queries = ["What key insights are presented on this page?"]

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

# 4. Generate multi-vector embeddings and score
with torch.inference_mode():
    image_embeddings = model(**image_batch)
    model.rope_deltas = None  # required before query forward
    query_embeddings = model(**query_batch)

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

⚠️Important: Bothmodel\.enable\_bidirectional\_attention\(\)and resettingmodel\.rope\_deltas = Noneprior to query forward passes are required to replicate the checkpoint’s full retrieval performance.

https://huggingface.co/tencent/EVIE-Preview-4.5B#cli-scoring-toolCLI Scoring Tool

You can also use the includedinfer\.pyscript directly:

python infer.py --query "Quarterly revenue report" --image document_page_1.png --image document_page_2.png

https://huggingface.co/tencent/EVIE-Preview-4.5B#reproducingReproducing

Run the end-to-end evaluation benchmark across all visible GPUs:

bash reproduce.sh

https://huggingface.co/tencent/EVIE-Preview-4.5B#notesNotes

• Automatic Dataset Download: On the first run,reproduce\.shautomatically invokesdownload\_data\.pyto fetch the 22 public ViDoRe datasets (~55 GB) from Hugging Face.
• Custom Dataset Path: To reuse an existing dataset directory, pass it directly:bash reproduce.sh /path/to/vidore
• Target Aggregates:

ViDoRe V1        nDCG@5    91.72  (10 tasks)
ViDoRe V2        nDCG@5    71.44  (4 tasks)
ViDoRe V1+V2     nDCG@5    85.93  (14 tasks)
ViDoRe V3 public nDCG@10   64.40  (8 domains x 6 languages)

https://huggingface.co/tencent/EVIE-Preview-4.5B#training-detailsTraining Details

EVIE was trained on approximately0.8 million high-quality image-query pairscovering multilingual documents, technical reports, complex financial tables, infographics, and document visual QA.

https://huggingface.co/tencent/EVIE-Preview-4.5B#hard-negative-mining–data-filteringHard Negative Mining & Data Filtering

• Dynamic Mining & Verification: Hard negatives are actively mined using intermediate retrievers and re-verified:- Candidates that accurately answer the query are promoted topositives. - Partially relevant or ambiguous candidates aremaskedout of the loss. - Only strictly irrelevant pages are retained as truehard negatives.
• Quality Filtering: Rows containing empty queries, corrupted images, or degraded text are systematically discarded.

https://huggingface.co/tencent/EVIE-Preview-4.5B#acknowledgementsAcknowledgements

• Built upon theColPali Enginedeveloped by Illuin Technology.
• Powered by theQwen3.5-4Bvision-language backbone.
• Evaluated on theViDoRe Benchmarkfamily.

https://huggingface.co/tencent/EVIE-Preview-4.5B#citationCitation

@misc{tencent2026evie,
  title        = {EVIE-Preview-4.5B},
  author       = {{Tencent}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/tencent/EVIE-Preview-4.5B}},
  note         = {Multilingual visual document retrieval with compact multi-vector embeddings}
}