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vidore/colpali-v1.3-hf

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Primitive: /encode ยท Encode ยท PaliGemma

> [!IMPORTANT] > This version of ColPali should be loaded with the `transformers ๐Ÿค—` release, not with `colpali-engine`. > It was converted using the `convert_colpali_weights_to_hf.py` script > from the `vidore/colpali-v1.3-merged` checkpoint.

MultimodalMulti-vector

Overview

Hardware: โ€” drives latency, throughput & cost

Size3.0B params
Tasks /encode
Licensegemma
Languagesen
Latency582 ms
Throughput23.0 mpix/s
Costโ€” /1M tok

Cost is approximate โ€” computed from list GPU prices; your actual price depends on the provider you deploy SIE with.

Embedding

Output typesMulti-Vec
Dimensionsmultivector: 128
Max sequence length2,048
Inputstext ยท image

Benchmarks

Vidore3ComputerScienceRetrieval

technology retrieval en

Visual document retrieval on computer science papers and slides

Performance L4 b1 c16
Corpus 23.2 mpix/s
Corpus p50 579.6ms
Query 484 tok/s
Query p50 266.9ms
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Vidore3FinanceEnRetrieval

finance retrieval en

Visual document retrieval on financial reports

Performance L4 b1 c16
Corpus 22.8 mpix/s
Corpus p50 583.7ms
Query 469 tok/s
Query p50 252.6ms
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Vidore3HrRetrieval

general retrieval en

Visual document retrieval on HR-related documents

Performance L4 b1 c16
Corpus 23.5 mpix/s
Corpus p50 585.1ms
Query 562 tok/s
Query p50 261.5ms
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Vidore3PharmaceuticalsRetrieval

medical retrieval en

Visual document retrieval on pharmaceutical documents

Performance L4 b1 c16
Corpus 16.3 mpix/s
Corpus p50 575.6ms
Query 538 tok/s
Query p50 250.7ms
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Open source inference for agents

Open-source inference for the models behind your agents. Run it yourself, or let us run it for you.

Github 2.1K

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