Article of the week
BERT: In-depth exploration of Architecture, Workflow, Code, and Mathematical Foundations by Jaiganesan
In the event you’ve been within the AI subject for some time, you’ve probably come throughout BERT a number of instances. Launched in 2018, BERT has been a subject of curiosity for a lot of, with many articles and YouTube movies trying to interrupt it down. Nevertheless, this text takes a distinct method by delving into Embeddings, Masked Language Mannequin Duties, Consideration Mechanisms, and Feed-Ahead Networks.
Our must-read articles
1. A Novel Retrieval-Augmented Generation with Autoencoder-Transformed Embeddings by Shenggang Li
It’s frequent to make use of direct RAG strategies just like the shortest cosine distance retriever. Nevertheless, these strategies can lead to irrelevant prompts as a result of noise within the data base. By the tip of this publish, you’ll perceive the way to use RAG with Autoencoder-Reworked Embeddings, a technique proposed right here. The writer additionally contains experimental knowledge, mathematical background, and proofs to help this method.
2. Want to Learn Quantization in The Large Language Model? By Milan Tamang
Quantization is a technique of compressing a bigger measurement mannequin (LLM or any deep studying mannequin) to a smaller measurement. On this article, you’ll study in regards to the what and why of quantization. Subsequent, you’ll dive in additional to know the how of quantization with some easy mathematical derivations. Lastly, we’ll write some code collectively in PyTorch to carry out quantization and de-quantization of LLM weight parameters.
3. Understanding Mamba and Selective State Space Models (SSMs) by Matthew Gunton
The Transformer structure has been the muse of most main massive language fashions (LLMs) available on the market at this time, delivering spectacular efficiency and revolutionizing the sphere. On this weblog, we’ll discover a novel block structure that goals to realize the facility of LLMs with out the scalability limitations of conventional Transformers.
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