The LLama Effect: How an Accidental Leak Sparked a Series of Impressive Open Source Alternatives to ChatGPT
Was this email forwarded to you? Sign up here The LLama Effect: How an Accidental Leak Sparked a Series of Impressive Open Source Alternatives to ChatGPTSundays, The Sequence Scope brings a summary of the most important research papers, technology releases and VC funding deals in the artificial intelligence space.Next Week in The Sequence
📝 Editorial: The LLama Effect: How an Accidental Leak Sparked a Series of Impressive Open Source Alternatives to ChatGPTThe 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. 🔎 ML ResearchOpenAI 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. BloombergGPT 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. Koala 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. BayesOpt for Hyperparameter OptimizationGoogle 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. 🤖 Cool AI Tech ReleasesVicunaVicuna is an open source Chatbot based on Meta AI Llama which matches ChatGPT quality —> Read more. ColossalChatThe team from the Colossal-AI project open sourced ColossalChat, an open source clone of ChatGPT with RLHF capabilities —> Read more. 🛠 Real World MLGenerative AI at LinkedInLinkedin discusses some of the lessons learned and best practices for building generative AI application —> Read more. Lyft RecommendationsLyft discusses the ML models and architecture used in their recommendation systems —> Read more. 📡AI Radar
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