Training LLMs with AMD MI250 GPUs and MosaicML
With the release of PyTorch 2.0 and ROCm 5.4, we are excited to announce that LLM training works out of the box on AMD MI250 accelerators with zero code changes and at high performance!
MosaicML Agrees to Join Databricks to Power Generative AI for All
Together with Databricks, we can bring our customers and community to the forefront of AI faster than ever before.
Introducing AI2 OLMo (Open Language Model)
Last month, the Allen Institute for AI (AI2) announced the development of an open, state-of-the-art generative language model: AI2 OLMo (Open Language Model), developed in partnership with MosaicML, is expected in early 2024.
Cloudflare R2 and MosaicML: Train LLMs on Any Compute with Zero Switching Costs
Together, Cloudflare and MosaicML give users the freedom to train LLMs on any compute, anywhere in the world, for faster, cheaper training runs without vendor lock-in.
Train and Deploy Generative AI Faster with MosaicML and Oracle
Generative AI models have taken the world by storm—but their use in enterprise environments is still limited. Why? This blog post explains the obstacles to adoption and discusses why the MosaicML platform running on Oracle Cloud Infrastructure (OCI) is the best solution for enterprises that want to operationalize generative AI.
Benchmarking Large Language Models on NVIDIA H100 GPUs with CoreWeave (Part 1)
The research and engineering teams here at MosaicML collaborated with CoreWeave, one of the leading cloud providers for NVIDIA GPU-accelerated server platforms, to provide a preview of the performance that can be achieved when training large language models (LLMs) with NVIDIA H100 GPUs on the MosaicML platform.
MosaicML + Comet
We’ve integrated MosaicML Cloud and Composer with Comet's experiment tracking platform, so ML practitioners can easily log relevant metrics and metadata. Improve your speed and efficiency with an end-to-end solution that helps you visualize and track your training runs to get the best model for your needs in the shortest time. In this blog post, we will show how easy it is to monitor and log your training workloads on the MosaicML Cloud with Comet.
Train Faster & Cheaper on AWS with MosaicML Composer
Use Composer, our open-source training library, to reduce deep learning training time and cost on AWS. Composer makes it easy to use the latest and greatest training algorithms, composing them together to speed up training and improve model quality.