RSRahul Sai Indeevar Vinrahul-ai.hashnode.dev·1d ago · 19 min readLLL EvaluationStarting with the basic question, "What is Evaluation ?"Evaluation refers to the measurement of different properties in system's outputs. What sort of properties are we interested in measuring? AI as00
RSRahul Sai Indeevar Vinrahul-ai.hashnode.dev·Sep 16 · 10 min readParameter-Efficient Fine-Tuning: From LoRA to QLoRAFine-tuning Large Language Models (LLMs) used to be an option only for organizations with massive compute budgets. Full fine-tuning requires updating and storing every parameter, gradient, and optimiz20
RSRahul Sai Indeevar Vinrahul-ai.hashnode.dev·Sep 8 · 14 min readLLM Post-Training: From Full Fine-Tuning to PEFT, Adapters, and Soft PromptsLarge Language Models (LLMs) arrive with immense foundational knowledge acquired during pre-training. However, deploying them to specialized real-world domains—such as legal document parsing, medical 10
RSRahul Sai Indeevar Vinrahul-ai.hashnode.dev·Sep 8 · 12 min readDeep Dive into LLM Pre-Training: Paradigms, Mathematical Formulations, and Training ObjectivesBuilding effective Transformer-based Pretrained Language Models (T-PTLMs) requires understanding how models acquire general linguistic representations and adapt to specialized domains. Pre-training si10
RSRahul Sai Indeevar Vinrahul-ai.hashnode.dev·Sep 7 · 15 min readAdvanced Transformers: Positional Encodings (RoPE) & Efficient Attention Mechanisms (KV Caching, MQA, GQA, MLA)In modern Large Language Model (LLM) architectures, two foundational engineering bottlenecks dictate model scalability and inference latency: Representing Order: How self-attention mechanisms—which a13A
