Advanced LLM Engineering & Neural Architecture
Advanced technical guides on LLM fine-tuning, transformer mechanisms (LoRA, GQA, RoPE), and NLP systems. Master the engineering behind state-of-the-art linguistic models.
A comprehensive technical index exploring the frontier of Large Language Models. From foundational Transformer mechanisms and tokenization strategies to advanced fine-tuning (SFT, DPO) and inference optimization, these guides provide the mathematical and architectural rigor required for production-scale ML systems.
Categories
- LLM Engineering:
Alignment and fine-tuning techniques like SFT, DPO, KL Divergence, and LoRA. - Benchmarking & Inference:
Exploring reasoning walls, decoding strategies, context windows, and RAG. - Transformer Architectural Deep Dives:
Explore foundational transformer architectures such as self-attention, GQA, PE, and tokenization. - Natural Language Processing (NLP):
Engineering systems for linguistic understanding, from word embeddings and NER to advanced semantic analysis.
LLM Engineering
Engineering systems for linguistic understanding, from word embeddings and NER to advanced semantic analysis.
The Definitive Guide to LLM Fine-Tuning: Objectivee, Mechanisms, and Hardware
Master LLM fine-tuning with the framework for base model selection, tuning mechanisms, and hardware constraints
A comprehensive technical deep-dive into the methodologies of fine-tuning Large Language Models (LLMs).
This guide breaks down the transition from foundation models to task-specific experts, covering learning objectives like SFT and DPO, and efficient architectural mechanisms including LoRA, QLoRA, and ReFT.
Ideal for developers looking to optimize model performance while balancing GPU constraints and data requirements.
Regularizing LLMs with Kullback-Leibler Divergence
Master the exploration-exploitation trade-off in fine-tuning with KL divergence regularization
A deep dive into the mechanics of KL Divergence in machine learning. This article examines the geometric properties of the Bregman family, the asymmetric traits of forward vs. reverse KL, and practical PyTorch implementations for preventing policy collapse during fine-tuning.
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Benchmarking & Inference
The Reasoning Wall: A Comparative Benchmark of Llama 3.2 vs. Qwen 3
Stress-testing multi-hop logic chains using Multi-LogiEval, Process Benchmarking, and Thought-to-Output Ratios
Most LLM benchmarks fail to identify exactly where logical coherence collapses.
This report establishes a framework for measuring Reasoning Depth (d) across three task tiers. By evaluating Llama 3.2 and Qwen 3 through four granular metrics—including Robustness Coefficients and Thought-to-Output ratios—we identify the reasoning wall and provide architectural recommendations for production-scale deployment.
LLM Decoding Strategies: A Guide to Algorithms and Sampling Methods
Discover how major decoding methods and algorithms work and their usages with practical examples
A Large Language Model (LLM), especially those with a decoder-only architecture, is a system designed to generate text that mirrors human-like fluency and coherence.
DoLa Decoding: Mitigating LLM Hallucinations via Layer Contrast
Explore how DoLA (Decoding by Contrasting Layers) mitigates hallucinations in transformer-based LMs
Decoding by Contrasting Layers (DoLa) is an inference-time decoding method that enhances a model’s factual knowledge by intervening in the conditional probability step.
Optimizing LLM Performance: Context Window Impact on RAG Accuracy
Benchmarking context length for optimal accuracy in long-form retrieval-augmented generation (RAG)
The context window (or context length) defines the maximum number of tokens — including the input prompt, any system instructions, and the model’s generated response — that the LLM can simultaneously process and attend to during the autoregressive loop.
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Transformer Architectural Deep Dives
Tokenization Strategies for LLM Applications
Explore the mechanics like BPE and Unigram and how to choose suitable tokenizer for your LLM application
Tokenization is the bridge between human language and machine-readable vectors.
Choosing the right tokenizer impacts an LLM's capabilities. This technical guide breaks down the core architectures and provides a framework for selecting the best one for your task.
Transformer Architecture: Self-Attention & MLOps Guide
Exploring attention and its role in contextual text understanding with walkthrough examples
The transformer model revolutionizes natural language processing (NLP) by processing entire sequences at once, leveraging techniques like self-attention mechanism, positional encodings, and multi-head attention.
Beyond the Window: Benchmarking Positional Encoding (PE) for LLM Extrapolation
Scaling context windows via PE extrapolation on unseen sequence lengths
An architectural deep dive and synthetic benchmark of FAPE, LPE, RPE, and RoPE. Learn how different Positional Encoding methods impact a Transformer's ability to handle sequences 20x longer than its training context.
Grouped Query Attention (GQA): Balancing LLM Quality and Speed
Finding the perfect balance between MHA quality and MQA inference throughput
Grouped-Query Attention (GQA) is a type of attention mechanisms designed to reduce the memory bandwidth requirements and latency during the decoding phase.
Implementing Attention Approximation: Transformer Efficiency & Trade-offsr
Self-attention mechanisms and attention approximation techniques in Transformer
The Transformer architecture, introduced in the “Attention Is All You Need” paper, has revolutionized Natural Language Processing (NLP). Its core innovation, the self-attention mechanism, allows models to weigh the importance of different parts of the input sequence. However, the standard self-attention mechanism suffers from its computational complexity which scales quadratically (O(N²)) as the length of the input sequence N grows, creating a bottleneck especially in tasks with long N such as document summarization or high-resolution image processing. Attention approximation solve this challenge by reducing the complexity using various techniques.
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Natural Language Processing (NLP)
Engineering systems for linguistic understanding, from word embeddings and NER to advanced semantic analysis.
LLM Decoding Strategies: A Guide to Algorithms and Sampling Methods
Discover how major decoding methods and algorithms work and their usages with practical examples
A Large Language Model (LLM), especially those with a decoder-only architecture, is a system designed to generate text that mirrors human-like fluency and coherence.
Beyond Zero: A Guide to N-Gram Smoothing and Language Model Robustness
Practical applications of key smoothing algorithms in n-gram models
Discover why zero-frequency events break NLP models and how to implement smoothing strategies—from simple Add-k to state-of-the-art Kneser-Ney—to ensure your language models handle unseen data gracefully
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