Edge 462: What is Fast-LLM. The New Popular Framework for Pretraining your Own LLMs
Was this email forwarded to you? Sign up here Edge 462: What is Fast-LLM. The New Popular Framework for Pretraining your Own LLMsCreated by ServiceNow, the framework provides the key building blocks for pretraining AI models.
Pretraining foundation models is often perceived as a capability reserved for big AI labs. Compute coordination ,data orchestration, constant experiments and the AI talent requirement are some of the challenges that make pretraining AI models prohibited for most organizations. However, the emergence of trends such as small language models(SLMs) or sovereign AI have pushed the idea that many companies are going to be building proprietary AI models. In that sense, lowering the bar for pretraining foundation models. And yet, there are very few frameworks that streamline those processes for companies. Fast-LLM is an open-source library specifically designed for training Large Language Models (LLMs) with a focus on speed, scalability, and cost-efficiency. Developed by ServiceNow Research’s Foundation Models Lab, Fast-LLM aims to empower AI professionals, researchers, and enterprises in pushing the boundaries of generative AI. This essay provides a deep dive into Fast-LLM, highlighting its key capabilities and core architectural components. Key Capabilities...Subscribe to TheSequence to unlock the rest.Become a paying subscriber of TheSequence to get access to this post and other subscriber-only content. A subscription gets you:
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Edge 460: Anthropic's New Protocol to Link AI Assistants to Data Sources
Thursday, December 26, 2024
Model Context Protocols is one of the recent AI contributions of the AI lab. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 459: Quantization Plus Distillation
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The Race for AI Reasoning is Challenging our Imagination
Sunday, December 22, 2024
New reasoning models from Google and OpenAI ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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