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Transformer inference powers tasks in NLP and vision, but is computationally intense, requiring optimizations. Large models like GPT-3 need extensive memory and FLOPs, with techniques like KV caching, quantization, and parallelism reducing costs.
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Open-source CodeLLMs like DeepSeek-Coder and Qwen2.5-Coder revolutionize code intelligence by offering repository-level training, multilingual support, and advanced features. These models rival proprietary solutions, fostering transparency, collaboration, and customization.
Dictionary & Showcase
Chunking enhances Retrieval-Augmented Generation (RAG) by splitting large texts into manageable parts for efficient processing in language models. This technique supports accurate responses, maintains context, and enables fast, parallel processing.
LLMs
The era of GPT-4's dominance is ending as diverse, specialized language models take the stage. Companies now favor both open-source and proprietary models tailored to specific tasks, moving away from one-size-fits-all solutions. Small Language Models (SLMs) offer efficiency for niche needs.
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As AI technology expands, so does its carbon footprint. This underscores the challenge: while AI accelerates digital transformation, it risks becoming a significant contributor to global CO₂ emissions.
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Generative AI offers new opportunities in content creation, but challenges like model selection and data complexity hinder developers. This article explores these issues and introduces Prem AI, a platform designed to simplify AI integration.