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README.md

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+# About Us
+
+With origins in academia and the open source community, Databricks was founded in 2013 by the original creators of Apache Spark™, Delta Lake and MLflow. 
+Our Data Intelligence Platform unifies data, AI and governance to make it easy for enterprises to create AI applications that understand their data.
+
+Bolstered by the 2023 acquisition of MosaicML, the combined AI R&D teams – creators of the Dolly models and [IFT dataset](http://databricks-dolly-15k), and the 
+[MPT family of models](https://huggingface.co/collections/mosaicml/mpt-6564f3d9e5aac326bfa22def) – 
+are now known as Databricks Mosaic AI Research. We continue to use rigorous science and engineering to deliver state-of-the-art generative AI training 
+and inference capabilities to organizations, while enabling them to retain control, security, and ownership over their valuable data.
+
+To get started with using models hosted on Hugging Face for training and inference on the Databricks platform, 
+[sign up for a free trial](https://www.databricks.com/try-databricks)!
+
+# Resources
+
+## [LLM Foundry](https://github.com/mosaicml/llm-foundry/tree/main)
+
+This repo contains code for training, finetuning, evaluating, and deploying LLMs for inference with [Composer](https://github.com/mosaicml/composer) 
+on the Databricks Data Intelligence Platform.
+
+## [Composer Library](https://github.com/mosaicml/composer)
+
+The open source Composer library makes it easy to train models faster at the algorithmic level. It is built on top of PyTorch. 
+Use our collection of speedup methods in your own training loop or—for the best experience—with our Composer trainer.
+
+## [StreamingDataset](https://github.com/mosaicml/streaming)
+
+Fast, accurate streaming of training data from cloud storage. We built `StreamingDataset` to make training on large datasets from cloud storage as fast, 
+cheap, and scalable as possible.
+
+It’s specially designed for multi-node, distributed training for large models—maximizing correctness guarantees, performance, and ease of use. 
+Now, you can efficiently train anywhere, independent of your training data location. Just stream in the data you need, when you need it. 
+To learn more about why we built `StreamingDataset`, read our [announcement blog](https://www.databricks.com/blog/mosaicml-streamingdataset).
+
+`StreamingDataset` is compatible with any data type, including images, text, video, and multimodal data.
+
+With support for major cloud storage providers, and designed as a drop-in replacement for your PyTorch 
+[`IterableDataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset) class, 
+`StreamingDataset` seamlessly integrates into your existing training workflows.
+
+## [Examples Repo](https://github.com/mosaicml/examples)
+
+This repo contains reference examples for training ML models quickly and to high accuracy. It's designed to be easily forked and modified. 
+It currently features the following examples:
+
+- [ResNet-50 + ImageNet](https://github.com/mosaicml/examples#resnet-50--imagenet)
+- [DeeplabV3 + ADE20k](https://github.com/mosaicml/examples#deeplabv3--ade20k)
+- [GPT / Large Language Models](https://github.com/mosaicml/examples#large-language-models-llms)
+- [BERT](https://github.com/mosaicml/examples#bert)