From IPFS pinning to dedicated IPFS gateways and IPNS names, Filebase provides all the tools you need to build the best decentralized applications.
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We provide anyone with a computer, the tools necessary to sample the electrical activity of their body. Our versatile and affordable bio-sensing microcontrollers can be used to sample electrical brain activity (EEG), muscle activity (EMG), heart rate (EKG), and much more. Our 3D-printable EEG headsets can be used to get research-grade EEG recordings.
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ã©ãããDA äºæ¥æ¬é¨ã®å¤§æ¾¤ã§ãã The fastest way to build custom ML toolsã¨è¬³ã£ã¦ããStreamlit ãããã£ã¦ã¿ã¾ãããHTMLãJSãCSSãããããã¨ãªããPythonã®ã¹ã¯ãªããããã¦ã§ãã¢ããªãä½ãã¦ä¾¿å©ãã«æåãã¾ãã! streamlit/streamlit: Streamlit â The fastest way to build custom ML tools ä»åã¯Streamlitã«å«ã¾ãããµã³ãã«ã¢ããªãåãããå¾ã«ãPandasã®ãã¼ã¿ãã¬ã¼ã ã使ã£ãç°¡åãªã¦ã§ãã¢ããªãä½ã£ã¦ã¿ãã®ã§ããã®å 容ãç´¹ä»ãã¾ãã ãã£ã¦ã¿ã ã¤ã³ã¹ãã¼ã« Streamlit 㯠pip ã§ã¤ã³ã¹ãã¼ã«ãããã¨ã§ä½¿ãã¾ãã
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This paper presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream natural language processing (NLP) tasks. We provide discussions and insights into the usage of LLMs from the perspectives of models, data, and downstream tasks. Firstly, we offer an introduction and brief summary of current GPT- and BERT-style LLMs.
How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools
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