Meet BLOOMChat: An Open-Source 176-Billion-Parameter Multilingual Chat Large Language Model (LLM) Built on Top of the BLOOM Model

With some great advancements being made in the field of Artificial Intelligence, natural language systems are rapidly progressing. Large Language Models (LLMs) are getting significantly better and more popular with each upgrade and innovation. A new feature or modification is being added nearly daily, enabling LLMs to serve in different applications in almost every domain. LLMs are everywhere, from Machine translation and text summarization to sentiment analysis and question answering. The open-source community has made some remarkable progress in developing chat-based LLMs, but mostly in the English language. A little less focus has been put on developing kind of similar
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Application of Large Language Models in Biotechnology and Pharmaceutical Research

ProGen Progen is a deep-learning LLM capable of generating protein sequences with a predictable function across large protein families. ProGen was trained on 280M protein sequences from more than 19,000 families, and the model is augmented with control tags specifying the property of the protein. ProGen can be fine-tuned to create more accurate protein sequences using specific sequences and tags. ChemCrow Although LLMs have shown great performance in tasks across various domains, they often struggle with chemistry-related problems. Additionally, these models do not have access to external sources, which limits their usefulness in scientific research. ChemCrow is an LLM chemistry

Meet Powderworld: A Lightweight Simulation Environment For Understanding AI Generalization

Despite recent advances in RL research, the ability to generalize to new tasks remains one of the major issues in both reinforcement learning (RL) and decision-making. RL agents perform remarkably in a single-task setting but frequently make mistakes when faced with unforeseen obstacles. Additionally, single-task RL agents can largely overfit the tasks they are trained on, rendering them unsuitable for real-world applications. This is where a general agent that can successfully handle various unprecedented tasks and unforeseen difficulties can be useful. The vast majority of general agents are trained using a variety of diverse tasks. Recent deep-learning research has shown