#Databricks

DBRX is a robust open-source language model by Databricks, featuring a Mixture-of-Experts architecture with 132 billion parameters suited for AI development. Offering resources like inference examples and model code, it integrates with platforms such as You.com and Perplexity Labs. It's optimized with libraries like Composer and MegaBlocks and supports both full and LoRA finetuning. Discover its broad capabilities within AI ecosystems through the Hugging Face repository for easy integration and customization.

Dolly, a large language model by Databricks with 12 billion parameters and based on EleutherAI's Pythia-12b, is commercially licensed. Fine-tuned on around 15,000 instruction-response pairs, Dolly excels in instruction adherence. Although not a state-of-the-art model, it targets accessibility and AI democratization. Challenges such as managing complex prompts and factual accuracy remain, with ongoing improvements. Available on Hugging Face, Dolly facilitates straightforward inference and training on diverse GPU configurations.

makeMoE is a build-from-scratch sparse mixture of experts language model, drawing inspiration from Andrej Karpathy's makemore. It employs PyTorch and features innovations like top-k gating and Kaiming He initialization, maintaining a focus on Shakespeare-like text generation. This project is ideal for those exploring scalable and customizable language models, with resources provided on HuggingFace for in-depth understanding and efficient training.

Discover how to create vision language models using Pytorch, with a focus on simplicity and education. This guide explores essential components like image encoding and language decoding, making it easy to develop and customize your own models. Use Databricks for robust performance and MLFlow for efficient tracking, and enjoy the project's educational approach and customization potential.