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  2. DBRX - Wikipedia

    en.wikipedia.org/wiki/DBRX

    DBRX is an open-sourced large language model (LLM) developed by Mosaic ML team at Databricks, released on March 27, 2024. [1] [2] [3] It is a mixture-of-experts Transformer model, with 132 billion parameters in total. 36 billion parameters (4 out of 16 experts) are active for each token. [4]

  3. Databricks - Wikipedia

    en.wikipedia.org/wiki/Databricks

    Databricks, Inc. is a global data, analytics, and artificial intelligence (AI) company founded by the original creators of Apache Spark. [ 3 ] The company provides a cloud-based platform to help enterprises build, scale, and govern data and AI, including generative AI and other machine learning models.

  4. Exclusive: Databricks is expanding the scope of its AI ... - AOL

    www.aol.com/finance/exclusive-databricks...

    When Andrew Ferguson launched Databricks’ corporate venture arm three years ago, he wasn’t expecting to be writing checks quite this fast. ... which DBRX was built on top of. Databricks ...

  5. Mixture of experts - Wikipedia

    en.wikipedia.org/wiki/Mixture_of_experts

    In March 2024, Databricks released DBRX. It is a MoE language model with 132B parameters, 16 experts, and sparsity 4. It is a MoE language model with 132B parameters, 16 experts, and sparsity 4. They also released a version finetuned for instruction following.

  6. Meet the riskiest AI models ranked by researchers - AOL

    www.aol.com/meet-riskiest-ai-models-ranked...

    The research shows DBRX Instruct—a Databricks product—consistently performed the worst by all metrics, TeamAI reports. For example, AIR-Bench scrutinized an AI model's safety refusal rate.

  7. List of artificial intelligence projects - Wikipedia

    en.wikipedia.org/wiki/List_of_artificial...

    DBRX, 136 billion parameter open sourced large language model developed by Mosaic ML and Databricks. [66] Speech recognition CMU Sphinx, a group of ...

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  9. Activation function - Wikipedia

    en.wikipedia.org/wiki/Activation_function

    Logistic activation function. The activation function of a node in an artificial neural network is a function that calculates the output of the node based on its individual inputs and their weights.

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