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  • Edge 281: Our series about federated learning(FL) continues with an overview of cross-device FL, Google’s research about FL and differential privacy and the FedLab framework for FL simulation.

  • Edge 282: We deep dive into LangChain, the uber popular framework for LLM-based development.

The friction between open source and API-based distribution is one of the most interesting battles looming in the generative AI ecosystem. In the text-to-image domain, the release of Stable Diffusion clearly signaled that open source was a viable distribution mechanism for foundational models. However, the same cannot be said in the large language model (LLM) space, in which the biggest breakthroughs are coming from models like GPT-4, Claude, and Cohere, which are only available via APIs. The open source alternatives to these models haven’t shown the same level of performance, specifically in their ability to follow human instructions. However, an unexpected research breakthrough and a leaked release are starting to change that.

A few weeks ago, Meta AI announced Llama, an LLM designed to advance research in the space. Llama was released in different versions, including 7B, 13B, 33B, and 65B parameters, and despite being notoriously smaller than alternative models, was able to match the performance of GPT-3 across many tasks. Llama was not initially open-sourced, but a week after its release, the model was leaked on 4chan, sparking thousands of downloads.

What could have been seen as an unfortunate incident has become one of the most interesting sources of innovation in the LLM space in the last few weeks. Since the leak of Llama, we have seen an explosion of innovation in LLM agents built on it. Just to cite a few examples:Stanford University released Alpaca, an instruction following model based on LLama 7B model.

Several other projects are worth mentioning in this list, and I am sure more will be released soon. One thing is certain: the accidental leak of Llama might have turned out to be one of the biggest sparks of innovation in the open source LLM space.

OpenAI Safety

OpenAI published a detailed blog post outlining some of the principles used to ensure safety in their models. The post emphasize in areas such as privacy, factual accuracy and harmful content prevention which are essential for the wide adoption of foundation models —> Read more.


Bloomberg published a paper introducing BloombergGPT, a 50 billion LLM fine tuned in financial data. The model is based on BLOOM and fine tuned on a 363 billion token dataset —> Read more.

Segment Anything

Meta AI  published a paper outlining the Segment Anything Model(SAM), a large scale model for image segmentation. The model was open sourced together with Segment Anything 1-Billion mask dataset (SA-1B), the largest computer vision segmentation ever released —> Read more.


Berkeley AI Research(BAIR) released a paper detailing Koala, a dialogue model fine tuned for academic research. The model is based on Meta AI’s Llama and matches the performance of ChatGPT —> Read more.

Google Research published a paper that models hyperparameter optimization as a Bayesian optimization problem. The paper proposes Hyper BayesOpt, a hyperparameter optimization algorithm that removes the need quantifying model parameters for Gaussian processes in BayesOpt —> Read more.

Vicuna is an open source Chatbot based on Meta AI Llama which matches ChatGPT quality —> Read more.

The team from the Colossal-AI project open sourced ColossalChat, an open source clone of ChatGPT with RLHF capabilities —> Read more.

Linkedin discusses some of the lessons learned and best practices for building generative AI application —> Read more.

Lyft discusses the ML models and architecture used in their recommendation systems —> Read more.

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