Introducing Idefics2: A Powerful 8B Vision-Language Model for the community
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Hugo Laurençon
HugoLaurencon
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Idefics2 is trained mostly on OBELICS, our open interleaved image-text document dataset.
Training on interleaved data is crucial to reaching high performance on VQA tasks, taking an arbitrary number of images as input, and doing in-context learning.
Dataset: HuggingFaceM4/OBELICS
Nomic visualization: https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f
Link to OBELICS thread: https://twitter.com/HugoLaurencon/status/1694005892839006301
Training on interleaved data is crucial to reaching high performance on VQA tasks, taking an arbitrary number of images as input, and doing in-context learning.
Dataset: HuggingFaceM4/OBELICS
Nomic visualization: https://atlas.nomic.ai/map/f2fba2aa-3647-4f49-a0f3-9347daeee499/ee4a84bd-f125-4bcc-a683-1b4e231cb10f
Link to OBELICS thread: https://twitter.com/HugoLaurencon/status/1694005892839006301
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The Cauldron is a massive collection of 50 high-quality datasets, all converted to the user/assistant format, and ready to use to fine-tune any Vision Language Model.
The Cauldron covers a wide range of tasks, including general visual question answering, counting, captioning, text transcription, document understanding, chart/figure understanding, table understanding, visual reasoning, geometry, spotting differences between 2 images or converting a screenshot to a code.
HuggingFaceM4/the_cauldron
The Cauldron covers a wide range of tasks, including general visual question answering, counting, captioning, text transcription, document understanding, chart/figure understanding, table understanding, visual reasoning, geometry, spotting differences between 2 images or converting a screenshot to a code.
HuggingFaceM4/the_cauldron
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