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      Cerebras Releases Open Source ChatGPT-Like Models for Free : Details Inside

      Cerebras, the Artificial intelligence chip startup Systems said it release open source ChatGPT-like models for the research and business community to use for free in an effort to foster more collaboration.

      Silicon Valley-based Cerebras release seven models all train on its AI supercomputer call as Andromeda, including smaller 111 million parameter language models to a larger 13 billion parameter model.

      Andrew Feldman, founder, and CEO of Cerebras Said :

      “There is a big movement to close what has been open-sourced in AI…it’s not surprising as there’s now huge money in it,”.

      “The excitement in the community, the progress we’ve made, has been in large part because it’s been so open.”

      Models with more parameters are able to perform more complex generative functions.

      OpenAI’s chatbot ChatGPT launch late 2022, for example, has 175 billion parameters and can produce poetry and research, which has help draw large interest and funding to AI more broadly.

      Cerebras said the smaller models can be deploy on phones or smart speakers while the bigger ones run on PCs or servers, although complex tasks like large passage summarization require larger models.

      So, Karl Freund, a chip consultant at Cambrian AI, said bigger is not always better.

      Karl Freund Said :

      “There’s been some interesting papers publish that show that (a smaller model) can be accurate if you train it more,”.

      “So there’s a trade off between bigger and better trained.”

      Karl Freund said his biggest model took a little over a week to train, work that can typically take many months, due to the architecture of the Cerebras system, which includes a chip the size of a dinner plate built for AI training.

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      Most of the AI models today are trained on Nvidia’s chips, but more and more startups like Cerebras are trying to take share in that market.

      The models trained on Cerebras machines can also be use on Nvidia systems for further training or customization, said Andrew Feldman.

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