Advanced LLM Engineering

Описание и применение

<h1> Advanced LLM Engineering </h1> <p><em>A deep-dive walkthrough of what is under the hood of large language models and how that knowledge changes the way you build with them — covering transformer architecture, self-attention and multi-head attention, positional encoding, tokenization algorithms, context window management, the KV cache, temperature/top-K/top-P sampling, quantization, distillation, fine-tuning vs. RAG, LoRA and QLoRA, small language models, open-weight vs. proprietary models, multimodal LLMs, and model routing and fallback — with C# code for the mechanics, back-of-envelope c

Задача

<h1> Advanced LLM Engineering </h1> <p><em>A deep-dive walkthrough of what is under the hood of large language models and how that knowledge changes the way you build with them — covering transformer architecture, self-attention and multi-head attention, positional encoding, tokenization algorithms, context window management, the KV cache, temperature/top-K/top-P sampling, quantization, distillation, fine-tuning vs. RAG, LoRA and QLoRA, small language models, open-weight vs. proprietary models, multimodal LLMs, and model routing and fallback — with C# code for the mechanics, back-of-envelope c

Результат

<h1> Advanced LLM Engineering </h1> <p><em>A deep-dive walkthrough of what is under the hood of large language models and how that knowledge changes the way you build with them — covering transformer architecture, self-attention and multi-head attention, positional encoding, tokenization algorithms, context window management, the KV cache, temperature/top-K/top-P sampling, quantization, distillation, fine-tuning vs. RAG, LoRA and QLoRA, small language models, open-weight vs. proprietary models, multimodal LLMs, and model routing and fallback — with C# code for the mechanics, back-of-envelope c

Доказательства

Advanced LLM Engineering

Источник