Best of Transformers — 2025

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   Data Engineer Things·1y

   10 minutes are all you need to understand how Transformers work in LLM

   Understanding how transformers work in large language models (LLMs) can be achieved quickly by breaking down the steps involved in the process. Starting from tokenization, where input data is converted into tokens, these tokens are then embedded into numerical representations understood by the model. These embeddings are processed through multiple transformer layers that use attention mechanisms to determine the importance of each token in relation to others. Finally, the processed data is projected back onto the vocabulary to predict the next token in a sequence. This foundational knowledge helps in exploring further intricacies of models like GPT-2.

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   Article

   Machine Learning Mastery·1y

   3 Easy Ways to Fine-Tune Language Models

   The post discusses three methods to fine-tune language models: full fine-tuning, parameter-efficient fine-tuning (PEFT), and instruction tuning. Full fine-tuning updates all model parameters, offering state-of-the-art performance but requiring significant computational power. PEFT, including techniques like LoRA, updates only a small portion of parameters, making it resource-efficient. Instruction tuning uses diverse task instructions, enhancing the model's ability to generalize. Code examples and detailed steps are provided for each method.

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   Article

   Machine Learning Mastery·1y

   Advanced Techniques to Build Your RAG System

   Learn advanced techniques to optimize retrieval-augmented generation (RAG) systems, focusing on improving query prompts, hybrid retrieval methods, and implementing multi-stage retrieval with re-ranking to enhance document retrieval and generation quality.

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   Article

   Towards Data Science·29w

   We Didn’t Invent Attention — We Just Rediscovered It

   Attention mechanisms in AI transformers aren't novel inventions but rediscoveries of fundamental optimization principles. The same mathematical pattern—selective amplification combined with normalization—emerges independently across evolution (500+ million years of neural systems), chemistry (autocatalytic reactions), and AI (gradient descent). This convergence suggests attention represents a universal solution to information processing under energy constraints. Reframing attention as amplification rather than selection offers practical insights for improving AI architectures: decoupling amplification from normalization, exploring non-content-based amplification, implementing local normalization pools, and designing systems that operate at critical dynamics for optimal information processing.

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   Article

   Hacker News·25w

   LLMs are a failure. A new AI winter is coming.

   Large Language Models (LLMs) face fundamental limitations that make them unsuitable for most practical applications. The core issue is that transformers generate plausible-sounding output by predicting the next token, which inevitably leads to hallucinations when the model lacks relevant training data. This results in a 5-40% failure rate that cannot be eliminated through scaling or fine-tuning. The author predicts an imminent AI bubble burst, with corporate AI projects failing at a 95% rate, similar to the dot-com crash. While some use cases will survive, the technology's inability to reliably distinguish correct from incorrect output makes it dangerous for critical applications like medicine, education, and law enforcement.

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   Article

   Medium·44w

   SmolLM3 : The best small LLM for everything

   SmolLM3 is a 3-billion parameter language model from Hugging Face that outperforms larger models through extensive training on 11.2 trillion tokens. Key features include extended thinking mode for step-by-step reasoning, native 64k token context length (extendable to 128k), multilingual support for six languages, and built-in tool calling capabilities. The model excels in benchmarks for math, reasoning, and programming tasks while being deployable on edge devices and single-GPU setups through various frameworks like transformers, vLLM, and llama.cpp.

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   Article

   Hugging Face·25w

   Transformers v5: Simple model definitions powering the AI ecosystem

   Hugging Face releases Transformers v5, marking five years since v4 with daily installs growing from 20,000 to 3 million. The library now supports over 400 model architectures and 750,000 community checkpoints. Version 5 focuses on simplicity through modular design, improved training support for both pre-training and fine-tuning, enhanced inference capabilities with continuous batching and a new serving API, and first-class quantization support. The release emphasizes interoperability across the ecosystem, enabling seamless integration with inference engines like vLLM and SGLang, local deployment tools like llama.cpp and MLX, and training frameworks like Unsloth and Axolotl.

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   Collections·1y

   Learn to Code Your Own Llama 4 LLM From Scratch

   Meta's Llama 4 features groundbreaking advancements in large language models. A new freeCodeCamp YouTube course by Vuk Doshik teaches how to implement Llama 4 from scratch, covering essential concepts and advanced techniques such as attention mechanisms, Rotary Positional Embeddings, and the Mixture-of-Experts design. The course provides practical coding instructions, making it accessible for beginners and valuable for aspiring AI engineers.

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   Hacker News·29w

   MoonshotAI/Kimi-Linear

   Kimi Linear introduces a hybrid linear attention architecture featuring Kimi Delta Attention (KDA), a refined version of Gated DeltaNet with improved gating mechanisms. The 48B parameter model (3B activated) supports 1M token context length, reduces KV cache requirements by 75%, and achieves 6x faster decoding throughput compared to traditional attention methods. Released as open-source with model checkpoints trained on 5.7T tokens, it demonstrates superior performance on long-context tasks while maintaining efficiency through a 3:1 KDA-to-global MLA ratio.

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    Article

    Medium·49w

    Top Ultimate List of 50 LLMs Interview Question • Master LLMs, Crack Your Next Interview

    A comprehensive collection of 50 interview questions covering Large Language Models fundamentals, from basic concepts like tokenization and attention mechanisms to advanced topics like LoRA fine-tuning, RAG, and deployment challenges. Each question includes practical explanations with examples, covering technical concepts like transformers, mathematical foundations, and real-world applications to help candidates prepare for LLM-focused technical interviews.

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    Hacker News·1y

    takara-ai/go-attention: A full attention mechanism and transformer in pure go.

    Takara.ai introduces the first pure Go implementation of attention mechanisms and transformer layers aimed at high performance and ease of use. The module supports various AI tasks like sequence-to-sequence translation, sentiment analysis, and financial forecasting, targeting applications in edge computing, real-time processing, cloud-native applications, and embedded systems. Features include dot-product attention, multi-head attention, and complete transformer layers. Future enhancements might include positional encoding and CUDA acceleration.

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    Collections·1y

    Building a Vision Transformer from Scratch

    Vision Transformers (ViTs) are transforming computer vision by using self-attention mechanisms, enhancing tasks like image classification, object detection, and image segmentation. This guide covers the core components and practical implementation of ViTs, including image preprocessing, patch embeddings, the multi-head attention mechanism, and assembling the complete model. It also offers a comparison between ViTs and other models like CLIP and SIGP to highlight their efficiency and flexibility.

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    Hugging Face·1y

    The Transformers Library: standardizing model definitions

    The Transformers library aims to be the central hub for model architectures across various frameworks, supporting over 300 models with consistent updates. It integrates with major training frameworks and inference engines, offering significant interoperability and efficiency. Efforts are underway to simplify model definitions and contributions to reduce complexity for model creators, enhancing ecosystem standardization.

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    Daily Dose of Data Science | Avi Chawla | Substack·1y

    Transformer vs. Mixture of Experts in LLMs

    Mixture of Experts (MoE) is an architecture used to enhance Transformer models by employing different 'experts' to improve performance. Transformers use feed-forward networks, while MoE models select a subset of smaller, specialized networks during inference, making operations faster. MoE faces training challenges such as some experts becoming under-trained. Solutions include adding noise to expert selection and limiting the number of tokens an expert processes. MoE models have more parameters but activate only a few during inference, leading to efficiency improvements.

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    Daily Dose of Data Science | Avi Chawla | Substack·1y

    Step-by-step Guide to Fine-tune Qwen3

    Alibaba has launched the next version of its large language model, Qwen 3. This tutorial guides readers on fine-tuning Qwen 3 using the Unsloth framework, employing techniques such as LoRA configuration, dataset preparation in conversational format, and step-by-step solutions for effective model training. The tutorial also includes running inference using the HuggingFace transformers library.

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