Tag: #transformer

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• Ring Attention Paper Analysis: Implementing Infinite Context Window Training in Distributed Environments

  Analyzes the Ring Attention paper exploring methods to overcome context length limitations in distributed environments. Covers the connection with Blockwise Parallel Transformer, implementation details, performance bench

  2026-03-08 · 33 min read #ai-papers#ring-attention#distributed-training#long-context#transformer

• Complete Guide to LLM Long-Context Performance and KV Cache Optimization: From MQA to Ring Attention

  A comprehensive practical guide covering KV Cache fundamentals for LLM long-context processing, memory consumption analysis, optimization techniques including MQA, GQA, PagedAttention, sliding window attention, and Ring

  2026-03-07 · 17 min read #llm#kv-cache#long-context#multi-query-attention#grouped-query-attention

• Mamba and State Space Model Paper Deep Dive: Transformer Alternative Architectures from Selective SSM to Mamba-2

  A comprehensive analysis of the SSM architecture covering the optional State Space Model mechanism of the Mamba paper, the development process from S4 to Mamba-2, the pros and cons of linear time complexity compared to T

  2026-03-07 · 30 min read #ai-papers#mamba#state-space-model#ssm#transformer

• RWKV Architecture Deep Dive: Linear Attention RNN That Rivals Transformers

  A comprehensive analysis of RWKV architecture covering the WKV attention mechanism, linear complexity advantages, comparison with Transformers and Mamba, training methodology, inference optimization, and practical deploy

  2026-03-07 · 19 min read #ai-papers#rwkv#linear-attention#rnn#transformer

• Build Your Own GPT — Training a Language Model from Scratch with nanoGPT

  Train a GPT language model from scratch using Andrej Karpathy's nanoGPT. A complete dissection of the Transformer architecture — tokenizers, Self-Attention, training loops — all with code.

  2026-03-03 · 7 min read #ai#llm#gpt#nanogpt#transformer

• Mamba Paper Review: Going Beyond Transformers with Selective State Space Models

  An in-depth review of the Mamba (Selective State Space Models) paper. Analyzing the evolution from S4 to Mamba, the Selective Scan mechanism, Hardware-Aware algorithms, and Mamba-2 State Space Duality with code examples.

  2026-03-02 · 8 min read #mamba#state-space-model#ssm#transformer#linear-attention

• Mamba: Linear-Time Sequence Modeling with Selective State Spaces — Paper Analysis

  A deep dive into the Mamba architecture, a promising alternative to Transformers. We explore the core idea behind Selective State Space Models, the hardware-aware algorithm, and experimental results along with code.

  2026-03-02 · 7 min read #ai-papers#mamba#ssm#state-space-model#transformer

• Complete Analysis of the GPT Series Papers: The Journey from GPT-1 to GPT-4, How Language Models Changed the World

  A complete generation-by-generation analysis of OpenAI GPT series. From GPT-1 unsupervised pre-training, GPT-2 zero-shot learning, GPT-3 in-context learning and scaling laws, InstructGPT RLHF, to GPT-4 multimodal capabil

  2026-03-01 · 36 min read #gpt#openai#language-model#transformer#pre-training

• Attention Is All You Need - A Complete Analysis of the Transformer Paper

  A thorough analysis of the "Attention Is All You Need" paper, the foundation of the Transformer architecture, breaking down its core mechanisms including Self-Attention, Multi-Head Attention, and Positional Encoding one

  2026-03-01 · 18 min read #ai-papers#transformer#deep-learning#attention

• FlashAttention: Optimizing Attention Through GPU Memory Hierarchy

  A review of the FlashAttention paper with an in-depth analysis of IO-aware attention optimization leveraging the GPU HBM/SRAM memory hierarchy.

  2026-03-01 · 18 min read #ai-papers#flash-attention#gpu#optimization#transformer

• Complete Analysis of the BERT Paper: How Bidirectional Transformers Changed the Landscape of NLP

  An in-depth analysis of Google BERT paper. We cover bidirectional pre-training through Masked Language Model (MLM) and Next Sentence Prediction (NSP), fine-tuning strategies, and the core principles of the architecture t

  2026-03-01 · 28 min read #bert#nlp#transformer#pre-training#fine-tuning

• Vision Transformer (ViT) Paper In-Depth Analysis: An Image is Worth 16x16 Words

  An in-depth analysis of Google ViT paper. We cover the innovative approach of converting images into patch sequences, the principles behind Patch Embedding and Position Embedding, the performance vs. data efficiency trad

  2026-03-01 · 30 min read #vit#vision-transformer#computer-vision#transformer#image-classification