Pixtral 12B
We introduce Pixtral-12B, a 12--billion-parameter multimodal language model.
Pixtral-12B is trained to understand both natural images and documents,
achieving leading performance on various multimodal benchmarks, surpassing a
number of larger models. Unlike many open-source models, Pixtral is also a
cutting-edge text model for its size, and does not compromise on natural
language performance to excel in multimodal tasks. Pixtral uses a new vision
encoder trained from scratch, which allows it to ingest images at their natural
resolution and aspect ratio. This gives users flexibility on the number of
tokens used to process an image. Pixtral is also able to process any number of
images in its long context window of 128K tokens. Pixtral 12B substanially
outperforms other open models of similar sizes (Llama-3.2 11B \& Qwen-2-VL 7B).
It also outperforms much larger open models like Llama-3.2 90B while being 7x
smaller. We further contribute an open-source benchmark, MM-MT-Bench, for
evaluating vision-language models in practical scenarios, and provide detailed
analysis and code for standardized evaluation protocols for multimodal LLMs.
Pixtral-12B is released under Apache 2.0 license.
670746ee3e510db7639040f2
Pravesh Agrawal
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2024-10-10T08:44:28.232Z
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Pravesh Agrawal
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Szymon Antoniak
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2024-10-10T08:44:33.689Z
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Szymon Antoniak
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Simontwice
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Emma Bou Hanna
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2024-10-10T08:44:39.014Z
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Emma Bou Hanna
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EmmaBH
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Devendra Chaplot
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2024-10-10T08:44:44.258Z
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Devendra Singh Chaplot
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devendrachaplot
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Jessica Chudnovsky
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Saurabh Garg
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Saurabh Garg
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Theophile Gervet
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2024-10-10T08:45:10.366Z
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Theophile Gervet
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Soham Ghosh
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2024-10-10T08:45:27.823Z
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Soham Ghosh
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Amélie Héliou
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Paul Jacob
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Albert Q. Jiang
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2024-10-10T08:45:46.987Z
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Albert Q Jiang
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Timothée Lacroix
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2024-10-10T08:46:00.153Z
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timothee lacroix
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Guillaume Lample
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2024-10-10T08:46:05.767Z
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Guillaume Lample
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Diego Las Casas
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2024-10-10T08:46:11.532Z
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Diego de las Casas
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Thibaut Lavril
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Thibaut Lavril
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Teven Le Scao
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2024-10-10T08:46:22.313Z
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Teven Le Scao
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Andy Lo
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Arthur Mensch
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Arthur Mensch
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Pavankumar Muddireddy
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Valera Nemychnikova
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Valera Nemychnikova
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Marie Pellat
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Marie Pellat
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Patrick Von Platen
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2024-10-10T08:47:46.655Z
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Patrick von Platen
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Nikhil Raghuraman
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Baptiste Rozière
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Baptiste Roziere
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Alexandre Sablayrolles
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Lucile Saulnier
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Romain Sauvestre
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Sauvestre
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Wendy Shang
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Wendy Shang
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Roman Soletskyi
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Roman Soletskyi
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Lawrence Stewart
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Lawrence Stewart
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Pierre Stock
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2024-10-10T08:49:26.789Z
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Pierre Stock
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Joachim Studnia
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Sandeep Subramanian
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Sandeep Subramanian
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Sagar Vaze
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Sagar Vaze
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Thomas Wang
Pixtral 12B
We introduce Pixtral-12B, a 12--billion-parameter multimodal language model.
Pixtral-12B is trained to understand both natural images and documents,
achieving leading performance on various multimodal benchmarks, surpassing a
number of larger models. Unlike many open-source models, Pixtral is also a
cutting-edge text model for its size, and does not compromise on natural
language performance to excel in multimodal tasks. Pixtral uses a new vision
encoder trained from scratch, which allows it to ingest images at their natural
resolution and aspect ratio. This gives users flexibility on the number of
tokens used to process an image. Pixtral is also able to process any number of
images in its long context window of 128K tokens. Pixtral 12B substanially
outperforms other open models of similar sizes (Llama-3.2 11B \& Qwen-2-VL 7B).
It also outperforms much larger open models like Llama-3.2 90B while being 7x
smaller. We further contribute an open-source benchmark, MM-MT-Bench, for
evaluating vision-language models in practical scenarios, and provide detailed
analysis and code for standardized evaluation protocols for multimodal LLMs.
Pixtral-12B is released under Apache 2.0 license.
Pixtral-12B, a 12-billion-parameter multimodal language model, excels in both natural language and image understanding, surpassing larger models and introducing an open-source benchmark for evaluation.
multimodal language model vision encoder natural resolution aspect ratio tokens long context window MM-MT-Bench vision-language models