Reinforcement Learning with Human Feedback (RLHF)
RLHF is a post-training recipe for turning a broadly capable language model into a more useful assistant. In practice, it […]
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large language model
BERT Variants: A Practical, Technical Guide
Think of BERT as a strong, general-purpose “reader” that turns text into contextual vectors. The moment you move from a
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How GitHub Copilot Works in Agent Mode
GitHub Copilot is evolving from in-editor code completion toward a software engineering assistant capable of independent action. In Agent Mode,
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Retrieval-Augmented Generation (RAG): A Practical Guide
Retrieval-Augmented Generation (RAG) is a technique that acts as an open-book exam for Large Language Models (LLMs). It allows a
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Mixture of Experts (MoE): Scaling Model Capacity Without Proportional Compute
Imagine you are building a house. You could hire one master builder who knows everything about construction, from plumbing and
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DeepSeek V3.2: Architecture, Training, and Practical Capabilities
DeepSeek V3.2 is one of the open-weight models that consistently competes with frontier proprietary systems (for example, GPT‑5‑class and Gemini
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ALiBi: Attention with Linear Biases
Imagine you are reading a mystery novel. The clue you find on page 10 is crucial for understanding the twist
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LLM Deployment: A Strategic Guide from Cloud to Edge
Imagine you have just built a high-performance race car engine (your Large Language Model). It is powerful, loud, and capable
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Rotary Positional Embedding (RoPE): A Deep Dive into Relative Positional Information
Rotary Positional Embeddings represent a shift from viewing position as a static label to viewing it as a geometric relationship. By treating tokens as vectors rotating in high-dimensional space, we allow neural networks to understand that “King” is to “Queen” not just by their semantic meaning, but by their relative placement in the text.
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Understanding KV Caching: The Key To Efficient LLM Inference
For Large Language Models (LLMs), inference speed and efficiency are paramount. One of the most critical optimizations for speeding up text generation is KV-Caching (Key-Value Caching).
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How Language Model Architectures Have Evolved Over Time
Introduction: The Quest to Understand Language Imagine a machine that could read, understand, and write text just like a human.
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How Large Language Model Architectures Have Evolved Since 2017
Imagine building a city: at first, you lay simple roads and bridges, but as the population grows and needs diversify,
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