Llama 2 and Code Llama are now on #KaggleModels!
@aiatmeta
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Kaggle Community Ready to Build with Llama 2
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We can't wait for the Kaggle community to start building with Llama 2!
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Excitement for SeamlessM4T Building Session
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Excited for this session — we can't wait to see what people build with SeamlessM4T!
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Meta Releases DINOv2 Training Code and Model Weights Under Apache-2
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We've expanding access to DINOv2 by releasing the training code and model weights under the Apache-2 license.
— AI at Meta (@AIatMeta) 11 septembre 2023
Details on this and more of our recent work to advance computer vision research and fairness in AI ⬇️We've expanding access to DINOv2 by releasing the training code and model weights under the Apache-2 license. Details on this and more of our recent work to advance computer vision research and fairness in AI
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Yann LeCun Selected for TIME100AI 2023 Recognition
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Congratulations @ylecun for being selected as one of the #TIME100AI for 2023. See the full list and a short interview with Yann on @TIME https://
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Anyscale Endpoints allows easy swapping between Llama 2 and closed models
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Anyscale Endpoints enables AI application developers to easily swap closed models for the Llama 2 models — or use open models along with closed models in the same application.
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Meta and Anyscale collaborate on Llama 2 developer opportunities
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Thanks for the collaboration — we're excited for what developers will be able to do with Llama 2 through Anyscale Endpoints!
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Meta Publishes Code Llama Research Paper on AI Foundation Models
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As part of our continued belief in the value of an open approach to today's AI, we've published a research paper with more information on Code Llama training, evaluation results, safety and more. Code Llama: Open Foundation Models for Code https://
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FACET: New Fairness Benchmark Dataset for Vision Models
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Last week we released FACET, a new comprehensive benchmark dataset for evaluating the fairness of models across a number of different vision tasks, constructed of 32K images from SA-1B, labeled by expert annotators.
— AI at Meta (@AIatMeta) 5 septembre 2023
Read the paper ➡️ https://t.co/OoYV2eiSYX pic.twitter.com/9Q7uk59TvTLast week we released FACET, a new comprehensive benchmark dataset for evaluating the fairness of models across a number of different vision tasks, constructed of 32K images from SA-1B, labeled by expert annotators. Read the paper https://
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Belebele: Multilingual Benchmark for High and Low-Resource Languages
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Belebele is composed of carefully crafted multiple-choice questions & answers based on FLORES-200 passages. This work enables the evaluation of NLP systems and large language models in high and low-resource languages. More details in the paper