running language models in parallel with each one focused on a sub-task, all orchestrated by a conductor language model picture something like a massive tree of GPT models working on answering a single complex prompt
LLMS
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Language Models with Dynamic Memory and Reflection Capabilities
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language models equipped with dynamic memory and the ability to reflect (e.g. Reflexion paper)
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Local Language Models Using Cloud Models as Plugins
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language models running locally on your machine that treat more complex language models in the cloud like plug-ins
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Using Language Models to Pre-process and Optimize Your Prompts
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using another language model for pre-processing your prompts to transform them into something that gives better responses if you want a prompt that you can use to already do this, read the prompt tip section here: https://
thepromptreport.com/p/report-5-eve
ryone-write-jailbreaks
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Replit partners with Google Cloud for 20M developers
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We're teaming up with @googlecloud
. Replit's 20M+ developers will get Google Cloud services, infrastructure, and foundation models. Idea to live software on Replit just got even faster. -
Cerebras-GPT Models Now Available on Hugging Face
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Cerebras-GPT models are available now on Hugging Face. https://
huggingface.co/cerebras You can also test drive Cerebras CS-2 systems via our Model Studio on the cloud. https://
cerebras.net/product-cloud/ (5/5) -
Cerebras-GPT Models Trained on CS-2 Systems
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The seven Cerebras-GPT models were trained on CS-2 systems using our simple, data-parallel Weight Streaming architecture, which allowed us to train these models in just a few weeks. (4/5)
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Cerebras-GPT Released Under Apache 2.0 License
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The AI industry is becoming increasingly closed. We believe in fostering open access to the most advanced models. Cerebras-GPT is being released under the Apache 2.0 license, allowing royalty-free use for research or commercial applications. (2/5)
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Cerebras-GPT introduces new scaling law for compute budgets
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One notable output of Cerebras-GPT is a new scaling law that predicts model performance for a given compute budget. This is the first scaling law derived using a public dataset. (3/5)