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www.anyscale.com | ||
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blog.vllm.ai
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pytorch.org
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| | | | | Large Language Models (LLMs) are typically very resource-intensive, requiring significant amounts of memory, compute and power to operate effectively. Quantization provides a solution by reducing weights and activations from 16 bit floats to lower bitrates (e.g., 8 bit, 4 bit, 2 bit), achieving significant speedup and memory savings and also enables support for larger batch sizes. | |
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predibase.com
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| | | | | From the coming wave of small language models to the future of fine-tuning and LLM architectures, these predictions represent the collective thoughts of our team of AI experts with experience building ML and LLM applications at Uber, AWS, Google, and more. | |
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www.analyticsvidhya.com
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| | | I tried to build a web-based To-Do app by vibe coding with Cursor AI, and I'll teach you how to install Cursor AI and use it for vibe coding. | ||
