Models › paligemma2 family

paligemma2-3b-pt-224

google Gemma Terms of Use out 23 months ago

3.03B parameters, images and text in, text out.

Verified 2026-10-25 Rank #582 in LLMs · score 74.3 method v1 Sources 3 Downloads 30d 6.7k · #528 of 619 tracked

Can I run it?1.8GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?Gemma Terms of UseGoogle's terms + prohibited-use policy; must pass terms downstreamlicence file2026-10-24
Do people use it?6.7kdownloads, 30 days#528 of 619 we track · below the median+0 likes, downloads +0% this week186 likesHugging Face2026-10-24

Worth reading

Picked by rule from 3 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Latest · 20 months ago

PaliGemma 2 mix: A vision-language model for multiple tasks

Google launched PaliGemma 2 mix checkpoints in February 2025, offering a vision-language model tuned for multiple tasks like captioning and OCR.

developers.googleblog.com 2025-02-19 2 HN pts 537 words about the paligemma2 family family

3 best of 3 tracked

The 1 pick above, plus these 2. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticlePaliGemma 2: Powerful Vision-Language Models, Simple Fine-Tuningdevelopers.googleblog.com · 2024-12-05 · 218 HN pts · official
  2. ArticlePaliGemma 2 – New vision language models by Googlehuggingface.co · 2024-12-05 · 1 HN pts

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 2 quantized builds and 4 Spaces.

  • As a service0no API provider listed
  • On own hardware2quantized builds
  • Built on233115 fine-tunes · 118 adapters · 0 merges
  • In apps4Hugging Face Spaces

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 16 GB card at INT4

About 1.8 GB at INT4, 3.6 GB at FP8, 7.3 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 1.8 GB of weights (estimate; KV cache and activations extra)INT41.8 GBFP8: about 3.6 GB of weights (estimate; KV cache and activations extra)FP83.6 GBBF16: about 7.3 GB of weights (estimate; KV cache and activations extra)BF167.3 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 1.8 GB of weights (estimate; KV cache and activations extra)INT41.8 GBFP8: about 3.6 GB of weights (estimate; KV cache and activations extra)FP83.6 GBBF16: about 7.3 GB of weights (estimate; KV cache and activations extra)BF167.3 GB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

Most coverage came in the first two weeks

Teal is the weights (2024-11-21), amber triangles are 3 dated articles (each links to it), grey ticks are repo commits.

DecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-12-05: 1 repo commit2024-11-21: 2 repo commitsPaliGemma 2: Powerful Vision-Language Models, Simple Fine-Tuning · 2024-12-05PaliGemma 2 mix: A vision-language model for multiple tasks · 2025-02-19PaliGemma 2 – New vision language models by Google · 2024-12-05Weights 2024-11-21 · 2024-11-21Weights 2024-11-21now
DecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-12-05: 1 repo commit2024-11-21: 2 repo commitsPaliGemma 2: Powerful Vision-Language Models, Simple Fine-Tuning · 2024-12-05PaliGemma 2 mix: A vision-language model for multiple tasks · 2025-02-19PaliGemma 2 – New vision language models by Google · 2024-12-05Weights 2024-11-21 · 2024-11-21Weights 2024-11-21now

What people built on it

235 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

235 derivatives on the Hub, most of them adapters

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

2 quantized (Hugging Face model tree)115 fine-tunes (Hugging Face model tree)118 adapters (Hugging Face model tree)2 quantized115 fine-tunes118 adapters
2 quantized (Hugging Face model tree)115 fine-tunes (Hugging Face model tree)118 adapters (Hugging Face model tree)2 quantized115 fine-tunes118 adapters

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

40 rows

Benchmarks 40 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (40)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported AI2D · Understand images and video74.7224-3Bmodel author2026-10-07
author-reported AI2D · Understand images and video83.1224-10Bmodel author2026-10-07
author-reported AI2D · Understand images and video83.2224-28Bmodel author2026-10-07
author-reported AI2D · Understand images and video76448-3Bmodel author2026-10-07
author-reported AI2D · Understand images and video84.4448-10Bmodel author2026-10-07
author-reported AI2D · Understand images and video84.6448-28Bmodel author2026-10-07
author-reported [AOKVQA-DA][aokvqa-da] (val)64.2224-3Bmodel author2026-10-07
author-reported [AOKVQA-DA][aokvqa-da] (val)68.9224-10Bmodel author2026-10-07
author-reported [AOKVQA-DA][aokvqa-da] (val)70.2224-28Bmodel author2026-10-07
author-reported [AOKVQA-DA][aokvqa-da] (val)67.9448-3Bmodel author2026-10-07
author-reported [AOKVQA-DA][aokvqa-da] (val)70.8448-10Bmodel author2026-10-07
author-reported [AOKVQA-DA][aokvqa-da] (val)71.2448-28Bmodel author2026-10-07
author-reported [AOKVQA-MC][aokvqa-mc] (val)79.7224-3Bmodel author2026-10-07
author-reported [AOKVQA-MC][aokvqa-mc] (val)83.7224-10Bmodel author2026-10-07
author-reported [AOKVQA-MC][aokvqa-mc] (val)84.7224-28Bmodel author2026-10-07
author-reported [AOKVQA-MC][aokvqa-mc] (val)82.5448-3Bmodel author2026-10-07
author-reported [AOKVQA-MC][aokvqa-mc] (val)85.9448-10Bmodel author2026-10-07
author-reported [AOKVQA-MC][aokvqa-mc] (val)87448-28Bmodel author2026-10-07
author-reported [ActivityNet-CAP][anet-cap]34.2224-3Bmodel author2026-10-07
author-reported [ActivityNet-CAP][anet-cap]35.9224-10Bmodel author2026-10-07
author-reported [ActivityNet-QA][anet-qa]51.3224-3Bmodel author2026-10-07
author-reported [ActivityNet-QA][anet-qa]53.2224-10Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (avg34)113.9224-3Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (avg34)115.8224-10Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (avg34)116.5224-28Bmodel author2026-10-07
Show all 40 rows (15 more)
BenchmarkValueSetting / notesReported byDate
author-reported [COCO-35L][coco-35l] (avg34)115.8448-3Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (avg34)117.2448-10Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (avg34)117.2448-28Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (en)138.4224-3Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (en)140.8224-10Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (en)142.4224-28Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (en)140.4448-3Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (en)142.4448-10Bmodel author2026-10-07
author-reported [COCO-35L][coco-35l] (en)142.3448-28Bmodel author2026-10-07
author-reported [COCOcap][coco-cap]141.3224-3Bmodel author2026-10-07
author-reported [COCOcap][coco-cap]143.7224-10Bmodel author2026-10-07
author-reported [COCOcap][coco-cap]144224-28Bmodel author2026-10-07
author-reported [COCOcap][coco-cap]143.4448-3Bmodel author2026-10-07
author-reported [COCOcap][coco-cap]145448-10Bmodel author2026-10-07
author-reported [COCOcap][coco-cap]145.2448-28Bmodel author2026-10-07

Use-case evidence

3 use cases

What it is good at 3 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

18 fields

Listed in LLMs

Facts

Pipeline tagimage-text-to-text
Librarytransformers
ArchitecturesPaliGemmaForConditionalGeneration
Model typepaligemma
Dtypebfloat16
Params by dtypeBF16: 3,032,242,416
Size bandM
MoEno
Gatedmanual
PapersarXiv:2407.07726, arXiv:2408.00118, arXiv:2310.09199, arXiv:2303.15343, arXiv:1706.03762, arXiv:2010.11929, arXiv:2412.03555, arXiv:2209.06794, arXiv:2209.04372, arXiv:2103.01913, arXiv:1908.04913, arXiv:1906.02467, arXiv:2203.10244, arXiv:2205.12522, arXiv:2104.12756, arXiv:1608.00272, arXiv:1511.02283, arXiv:1905.13648, arXiv:2110.11624, arXiv:2108.03353, arXiv:1810.12440, arXiv:1904.03493, arXiv:2010.04295, arXiv:1511.09207
LicenseGemma Terms of Use (gemma)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · PaliGemmaForConditionalGeneration · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: not in registry · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for paligemma2-3b-pt-224: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)7.3 GB · Single GPU (<24 GB)
FP8 (1 byte/param)3.6 GB · Edge (<4 GB)
INT4 (0.5 bytes/param)1.8 GB · Edge (<4 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.

All variants

40 variants

Lineage

Original model Family: google/paligemma2 235 derivatives on the Hub

finetune: 115quantized: 2adapter: 118

Top variants (40 shown, ranked by downloads)

Relation
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
IPEC-COMMUNITY/spatialvla-4b-224-pt finetune — 4.03B M mit IPEC-COMMUNITY 14 157.6k
mateoguaman/vamos finetune — 3.03B M cc mateoguaman 3 2.1k
onnx-community/paligemma2-3b-pt-224 quantized — — — — onnx-community 2 663
oliveirabruno01/count_intersection-ft-paligemma2-3b-pt-224 finetune — 3.03B M — oliveirabruno01 0 266
MagiBoss/PaliGemma2-3b-224-COCO adapter — — — gemma MagiBoss 3 99
aagha/paligemma2_json_extraction adapter — — — gemma aagha 0 247
mateoguaman/paligemma2_toy finetune — 3.03B M gemma mateoguaman 0 224
mateoguaman/vamos_navigation_only finetune — 3.03B M cc mateoguaman 1 168
arkrajkundu/knitGPT_v1.0 adapter — — — — arkrajkundu 3 62
StanfordAIMI/chexanatomy-paligemma-3b-224 adapter — — — gemma StanfordAIMI 1 120
TRUBETSKOY/paligemma_cathno_det finetune — 3.03B M gemma TRUBETSKOY 0 168
mateoguaman/vamos_lora finetune — — — cc mateoguaman 1 105
NSTiwari/paligemma2-onnx quantized — — — mit NSTiwari 0 151
hamzasibous/paligemma-msr adapter — — — gemma hamzasibous 0 149
mark-eidsaune/paligemma_sft adapter — — — gemma mark-eidsaune 0 145
TRUBETSKOY/paligemma_catheter_det_visft finetune — 3.03B M gemma TRUBETSKOY 0 129
jithinibosonreg/paligemma2_instance_segmentation_car_damage101images-9april2025 finetune — 3.03B M gemma jithinibosonreg 0 125
jithinibosonreg/paligemma2_instance_segmentation_car_damage750iages-10april2025 finetune — 3.03B M gemma jithinibosonreg 1 62
mateoguaman/vamos_navigation_only_lora finetune — — — cc mateoguaman 0 98
hendry64/paligemma2-qlora-odometer-224 adapter — — — gemma hendry64 1 37
EricChan1122/paligemma2-224-parasitic-egg-detection8 adapter — — — gemma EricChan1122 1 31
Mohit1Kulkarni/Fine-tuned-Paligemma2-Graph-Analysis-V2 adapter — — — gemma Mohit1Kulkarni 0 76
Mohammad-Umer7/ieee-recycling-advisor-v1 adapter — — — gemma Mohammad-Umer7 0 73
mateoguaman/paligemma2-3b-pt-224-sft-lora-every1-50pct-sub5 finetune — — — — mateoguaman 0 72
mateoguaman/paligemma2-3b-pt-224-sft-lora-magicsoup_with_iphone finetune — — — — mateoguaman 0 71
tbkhori/paligemma_vqav2 adapter — — — gemma tbkhori 1 20
arkrajkundu/knitGPT_PG_adapter adapter — — — — arkrajkundu 1 18
mrinalmouza1984/paligemma_output adapter — — — gemma mrinalmouza1984 0 66
mateoguaman/paligemma2-3b-pt-224-sft-lora-spot-only_sub5 finetune — — — — mateoguaman 0 62
Serialtechlab/paligemma2-dhivehi-ocr-full finetune — 3.03B M apache-2.0 Serialtechlab 1 9
Mohit1Kulkarni/Fine-tuned-Paligemma2-Graph-Analysis-V3 adapter — — — gemma Mohit1Kulkarni 0 55
Mohit1Kulkarni/Fine-tuned-Paligemma2-Graph-Analysis adapter — — — gemma Mohit1Kulkarni 0 52
mateoguaman/paligemma2-3b-pt-224-sft-full_every10_sub5 finetune — 3.03B M — mateoguaman 0 51
SebastianCAC/castell-paligemma2-locks-safes-v3 finetune — — — other SebastianCAC 1 0
axonhug/paligemma_vqav2 finetune — 3.03B M gemma axonhug 0 47
mateoguaman/paligemma2-3b-pt-224-sft-lora-vamos_25pct_traj_25pct_atraj_50pct_anno finetune — — — — mateoguaman 0 45
mateoguaman/paligemma2-3b-pt-224-sft-lora-magicsoup finetune — — — — mateoguaman 0 43
mateoguaman/paligemma2_racer finetune — 3.03B M gemma mateoguaman 0 40
mateoguaman/paligemma2_racer_correct_wh finetune — 3.03B M gemma mateoguaman 0 40
EricChan1122/paligemma2-224-parasitic-egg-detection3 adapter — — — gemma EricChan1122 0 40

Repository changelog

3 commits

Repository changelog 3 commits

DateCommitSHA
2024-12-05 create model card (#1) 96eeb17
2024-11-21 Upload folder using huggingface_hub 7fe80e2
2024-11-21 initial commit 6d88c07

Articles and coverage

3 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official3

developers.googleblog.com2024-12-05officialauthorityHN front pageabout the paligemma2 familyHN 218 pts · 27 comments468 words
Google introduces PaliGemma 2, the next evolution in tunable vision-language models built upon the Gemma 2 family. This update adds vision capabilities to make fine-tuning easier and offers multiple model sizes ranging from 3B to 28B parameters alongside resolutions of 224px, 448px, and 896px. The models function as a drop-in replacement for existing users, enabling detailed captioning and specialized tasks like chemical formula recognition without major code modifications.written, reviewed, cited
developers.googleblog.com2025-02-19officialauthorityabout the paligemma2 familyHN 2 pts · 0 comments537 words
Google launched PaliGemma 2 mix checkpoints in February 2025, offering a vision-language model tuned for multiple tasks like captioning and OCR. These models come in sizes of 3B, 10B, and 28B parameters and support resolutions of 224px and 448px. Users can explore capabilities on the Hugging Face demo, download weights from Kaggle and Hugging Face, or deploy directly in Vertex Model Garden.written, reviewed, cited
huggingface.co2024-12-05authorityabout the paligemma2 familyHN 1 pts · 0 comments1,778 words
Google releases PaliGemma 2, a new vision language model family combining SigLIP with Gemma 2. The models come in 3B, 10B, and 28B parameter sizes and support input resolutions of 224x224, 448x448, and 896x896. Fine-tuned variants on the DOCCI dataset achieve NES scores of 28.4 and 20.3 at 448x448 resolution.written, reviewed, cited

Papers

8 papers

Papers 8

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

Similar models

3 models

Similar models

  • paligemma-3b-pt-224 · 2.92B
    same size band (M), same task (image-text-to-text)
  • dots.ocr · 3.04B
    same size band (M), same task (image-text-to-text), permissive license
  • d1-3B · 3.12B
    same size band (M), same task (image-text-to-text)

Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:40 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:12 UTC · Last verified 2026-10-25

Follow paligemma2-3b-pt-224

Watch this model

Email alerts are not open yet; the feeds above are the same data. See feeds for the item schema.