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  1. 4 days ago · Ryght’s Journey to Empower Healthcare and Life Sciences with Expert Support from Hugging Face. ... We’re on a journey to advance and democratize artificial intelligence through open source and open science.

    • StackLLaMA

      Models such as ChatGPT, GPT-4, and Claude are powerful...

    • Code Llama

      Code Llama is a family of state-of-the-art, open-access...

  2. 3 days ago · May 9th, 2024, 10:52 AM PDT. Hugging Face CEO Clém Delangue explores the top issues and innovations surrounding AI, including the debate around open vs. closed source, transparency and regulation ...

    • 28 min
  3. 4 days ago · Clem Delangue, CEO and co-founder of Hugging Face, joins Caroline Hyde and Ed Ludlow on the sidelines of the Bloomberg Tech live event to discuss the company's momentum and how open-source LLMs ...

  4. 16 hours ago · In this tutorial, we will be using the short context version of the Phi-3 ONNX models ( Phi-3-mini-4k-instruct-onnx) and using the model available from Hugging Face. Before we begin, it is important to install the git large file system extension and the Hugging Face CLI. These tools are necessary for downloading the ONNX models.

  5. 16 hours ago · Hugging Face Autotrain – Getting Started. Autotrain is a no-code platform from Hugging Face to train, evaluate, and deploy machine learning and deep learning models. In this article, we will use Hugging Face Autotrain to train a Small Language Model (SLM). The Hugging Face Autotrain platform offers several functionalities for training:

  6. 5 days ago · The following graph shows different metrics collected from the CloudWatch log using TrainingJobAnalytics.. For more information about how to use the new SageMaker Hugging Face text classification algorithm for transfer learning on a custom dataset, deploy the fine-tuned model, run inference on the deployed model, and deploy the pre-trained model as is without first fine-tuning on a custom ...

  7. 2 days ago · Hugging Face is a platform for creating programs in the field of artificial intelligence, machine learning, and data science. It provides the infrastructure for implementing AI and ML technologies and provides access to language models and datasets. In addition, it provides tools for developing neural networks.

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