This content originally appeared on HackerNoon and was authored by Writings, Papers and Blogs on Text Models
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6 Phi-3-Vision
A Example prompt for benchmarks
6.1 Technical Specifications
Architecture The Phi-3-Vision (4.2B parameters) is a multimodal model designed to process an image and a textual prompt as inputs, and subsequently generate textual outputs. This model is composed of two primary components: an image encoder, i.e., CLIP ViT-L/14 [RKH+ 21] and a transformer decoder, i.e., phi-3-mini-128K-instruct. The visual tokens, once extracted by the image encoder, are then combined with text tokens in an interleaved way (no particular order for image and text tokens). To accommodate high-resolution images and various aspect ratios, a dynamic cropping strategy [DZZ+ 24b] is utilized to split the input image into a 2d array of blocks, where the tokens of the blocks are concatenated to represent the whole image.
\ Pre-training The Phi-3-Vision model undergoes a pre-training phase using a diverse dataset, which consists of a combination of interleaved image-text documents (e.g., [LST+ 24]), image-text pairs from FLD-5B [XWX+ 24], synthetic data derived from Optical Character Recognition (OCR) of PDF files, datasets for chart/table comprehension, and text-only data. The objective of predicting the next token is employed specifically on text tokens, while any loss associated with image tokens is disregarded during this phase. The pre-training process involves a total of 0.5T tokens that encompass both visual and text elements. During the pre-training phase, the maximum image resolution is capped at 1344 ×1344 as the majority of the training images are smaller than this resolution.
\ Post-training. The Phi-3-Vision model contains two post-training stages: supervised finetuning (SFT) and direct preference optimization (DPO). For SFT, we leveraged text SFT dataset, public multimodal instruct tuning datasets along with large-scale multimodal instruct tuning datasets that we built ourselves, covering diverse domains and tasks such as general natural image understanding, chart/table/- diagram understanding/reasoning, PowerPoint understanding, and model safety. The multimodal SFT data has about a total of 15B tokens. For DPO we mainly use a text DPO dataset and a relatively smaller-scale multimodal DPO dataset. For these two stages, we jointly train multimodal tasks and textonly tasks so that the model can achieve multi-modal reasoning while maintaining language capabilities as much as possible.
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:::info Authors:
(1) Marah Abdin;
(2) Sam Ade Jacobs;
(3) Ammar Ahmad Awan;
(4) Jyoti Aneja;
(5) Ahmed Awadallah;
(6) Hany Awadalla;
(7) Nguyen Bach;
(8) Amit Bahree;
(9) Arash Bakhtiari;
(10) Jianmin Bao;
(11) Harkirat Behl;
(12) Alon Benhaim;
(13) Misha Bilenko;
(14) Johan Bjorck;
(15) Sébastien Bubeck;
(16) Qin Cai;
(17) Martin Cai;
(18) Caio César Teodoro Mendes;
(19) Weizhu Chen;
(20) Vishrav Chaudhary;
(21) Dong Chen;
(22) Dongdong Chen;
(23) Yen-Chun Chen;
(24) Yi-Ling Chen;
(25) Parul Chopra;
(26) Xiyang Dai;
(27) Allie Del Giorno;
(28) Gustavo de Rosa;
(29) Matthew Dixon;
(30) Ronen Eldan;
(31) Victor Fragoso;
(32) Dan Iter;
(33) Mei Gao;
(34) Min Gao;
(35) Jianfeng Gao;
(36) Amit Garg;
(37) Abhishek Goswami;
(38) Suriya Gunasekar;
(39) Emman Haider;
(40) Junheng Hao;
(41) Russell J. Hewett;
(42) Jamie Huynh;
(43) Mojan Javaheripi;
(44) Xin Jin;
(45) Piero Kauffmann;
(46) Nikos Karampatziakis;
(47) Dongwoo Kim;
(48) Mahoud Khademi;
(49) Lev Kurilenko;
(50) James R. Lee;
(51) Yin Tat Lee;
(52) Yuanzhi Li;
(53) Yunsheng Li;
(54) Chen Liang;
(55) Lars Liden;
(56) Ce Liu;
(57) Mengchen Liu;
(58) Weishung Liu;
(59) Eric Lin;
(60) Zeqi Lin;
(61) Chong Luo;
(62) Piyush Madan;
(63) Matt Mazzola;
(64) Arindam Mitra;
(65) Hardik Modi;
(66) Anh Nguyen;
(67) Brandon Norick;
(68) Barun Patra;
(69) Daniel Perez-Becker;
(70) Thomas Portet;
(71) Reid Pryzant;
(72) Heyang Qin;
(73) Marko Radmilac;
(74) Corby Rosset;
(75) Sambudha Roy;
(76) Olatunji Ruwase;
(77) Olli Saarikivi;
(78) Amin Saied;
(79) Adil Salim;
(80) Michael Santacroce;
(81) Shital Shah;
(82) Ning Shang;
(83) Hiteshi Sharma;
(84) Swadheen Shukla;
(85) Xia Song;
(86) Masahiro Tanaka;
(87) Andrea Tupini;
(88) Xin Wang;
(89) Lijuan Wang;
(90) Chunyu Wang;
(91) Yu Wang;
(92) Rachel Ward;
(93) Guanhua Wang;
(94) Philipp Witte;
(95) Haiping Wu;
(96) Michael Wyatt;
(97) Bin Xiao;
(98) Can Xu;
(99) Jiahang Xu;
(100) Weijian Xu;
(101) Sonali Yadav;
(102) Fan Yang;
(103) Jianwei Yang;
(104) Ziyi Yang;
(105) Yifan Yang;
(106) Donghan Yu;
(107) Lu Yuan;
(108) Chengruidong Zhang;
(109) Cyril Zhang;
(110) Jianwen Zhang;
(111) Li Lyna Zhang;
(112) Yi Zhang;
(113) Yue Zhang;
(114) Yunan Zhang;
(115) Xiren Zhou.
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:::info This paper is available on arxiv under CC BY 4.0 DEED license.
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This content originally appeared on HackerNoon and was authored by Writings, Papers and Blogs on Text Models
Writings, Papers and Blogs on Text Models | Sciencx (2025-07-08T02:30:02+00:00) Unveiling phi-3-vision: Architecture, Pre-training, and Post-training for Visual AI. Retrieved from https://www.scien.cx/2025/07/08/unveiling-phi-3-vision-architecture-pre-training-and-post-training-for-visual-ai/
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