Best of Transformers — September 2024

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   Article

   Hugging Face·2y

   Fine-tuning LLMs to 1.58bit: extreme quantization made easy

   As large language models (LLMs) grow, reducing their computational and energy costs via quantization becomes crucial. BitNet, a new transformer architecture from Microsoft Research, drastically cuts computational costs by representing parameters with ternary values (-1, 0, 1) at 1.58 bits per parameter. The post details how existing models, like Llama3, can be fine-tuned using BitNet, achieving efficient performance while maintaining accuracy. The article also covers the implementation, optimization, and benchmarking of custom inference kernels, making LLMs more scalable and practical.

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2. 2
   Article

   Towards AI·2y

   Transformer Architecture Part -1

   Transformers have revolutionized deep learning, excelling in language and vision tasks. The core architecture consists of identical encoder and decoder blocks, each featuring self-attention, feed-forward neural networks, add & norm layers, and residual connections. The process begins with tokenization, text vectorization, and positional encoding. Multi-head attention then contextualizes these vectors, followed by normalization and passing through feed-forward networks. The architecture ensures efficient handling of complex data patterns while maintaining consistent dimensionality for smooth training.

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3. 3
   Article

   GoPenAI·2y

   Transformer from Scratch in TF Part 1: Embedding and Positional Encoding

   This post, the first part of a series, explores how to build a Transformer model from scratch using TensorFlow 2, focusing on embedding and positional encoding. It covers text tokenization using TensorFlow's TextVectorization layer, transforming text into numerical formats, and embedding words into vectors for machine language comprehension. The post also explains positional encoding to incorporate sequence information into embedding outputs, essential for the Transformer architecture. Through code demonstrations and visualizations, key concepts are clarified. Future posts will explore the Scaled Dot-Product Attention mechanism, a pivotal component of Transformers.

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   Article

   Community Picks·2y

   BART Model for Text Summarization

   BART (Bidirectional and Auto-Regressive Transformers) is a pre-training method combining the strengths of BERT and GPT models. It's designed as a denoising autoencoder useful for various NLP tasks, especially text summarization. BART follows a sequence-to-sequence paradigm, excelling in both comprehension and fine-tuned text generation tasks. HuggingFace provides easy access to pre-trained BART models for text summarization.

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   Article

   GoPenAI·2y

   Transformer from Scratch in TF Part 2: Encoder

   This post provides a detailed, step-by-step explanation of the Transformer Encoder Block using TensorFlow, focusing on the Multi-Head Attention mechanism. It covers the creation of Queries, Keys, and Values, the Scaled Dot-Product Attention mechanism, and the addition of residual connections and Layer Normalization. The final component, the Feed-Forward Network (FFN), is also detailed. Code examples in TensorFlow are provided throughout to illustrate key concepts.

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   Article

   Towards AI·2y

   #38 Back to Basics — RAG, Transformers, ML Optimization, and LLM Evaluation.

   The post delves into the relevance of RAG (Retrieval-Augmented Generation), comparing it against models like Gemini that process millions of tokens. It highlights why RAG will remain useful for specific applications. There's a mention of a free masterclass on AI tools, a project spotlight on an AI-driven job search assistant, and various collaboration opportunities in the AI community. A featurette on a Streamlit app for RAG evaluation and discussions on the importance of transformer architecture in NLP and querying SQL databases using LLM agents are also included.

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   Article

   Towards AI·2y

   Get The Most Out of Llama 3.1

   Llama 3.1, the first open model with nearly half a trillion parameters, introduces critical advancements in preprocessing, training configuration, and model alignment. Emphasizing the removal of toxic and redundant data, domain balancing, and gradual increase in batch size and sequence length, it aims for stability and computational efficiency. Annotations are refined for quality, and DPO is preferred over PPO for model alignment. Post-training, the model is fine-tuned for expertise in code, multilingual capabilities, and math reasoning, ensuring it only answers questions it is confident about.

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