Transformers and Language Model Implementation in PyTorch || IN HINDI

Описание: These sources provide an extensive overview of building and understanding language models, specifically focusing on PyTorch implementations and the underlying Transformer architecture. Several tutorials offer step-by-step guides, such as creating a simple character-level model to generate Pokémon names and a comprehensive roadmap for building a ChatGPT clone using Andrej Karpathy’s nanochat. The texts emphasize foundational concepts like Tensors, autograd, and the nn module in PyTorch, while a university assignment details the low-level implementation of a Neural Machine Translation system using encoders, decoders, and attention mechanisms. Additionally, a business-focused article contrasts Small Language Models (SLMs) with Large Language Models (LLMs), highlighting the efficiency, customization, and growing enterprise adoption of smaller, specialized models for various applications. Collectively, the documents function as educational, practical, and strategic resources for understanding and developing modern AI language models.

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