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The proliferation of deep learning in natural language processing (NLP) has led to the development and release of innovative technologies capable of understanding and generating human language with remarkable proficiency. Atinuke, a Transformer-based neural network, optimises performance across various language tasks by utilising a unique configuration. The architecture interweaves layers for processing sequential data with attention mechanisms to draw meaningful affinities between inputs and outputs. Due to the configuration of its topology and hyperparameter tuning, it can emulate human-like language by extracting features and learning complex mappings. Atinuke is modular, extensible, and integrates seamlessly with existing machine learning pipelines. Advanced matrix operations like softmax, embeddings, and multi-head attention enable nuanced handling of textual, acoustic, and visual signals. By unifying modern deep learning techniques with software design principles and mathematical theory, the system achieves state-of-the-art results on natural language tasks whilst remaining interpretable and robust.

Submission history

From: Abiodun Finbarrs Oketunji [view email]
[v1] Tue, 30 Jan 2024 04:29:48 UTC (110 KB)

||||I|||| Skip to main content We gratefully acknowledge support from the Simons Foundation, member institutions , and all contributors. Donate > cs > arXiv:2401.16736 Help | Advanced Search All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text Search GO quick links * Login * Help Pages * About Computer Science > Computation and Language arXiv:2401.16736 (cs) [Submitted on 30 Jan 2024] Title: Engineering A Large Language Model From Scratch Authors: Abiodun Finbarrs Oketunji Download a PDF of the paper titled Engineering A Large Language Model From Scratch, by Abiodun Finbarrs Oketunji Download PDF Abstract: The proliferation of deep learning in natural language processing (NLP) has led to the development and release of innovative technologies capable of understanding and generating human language with remarkable proficiency. Atinuke, a Transformer-based neural network, optimises performance across various language tasks by utilising a unique configuration. The architecture interweaves layers for processing sequential data with attention mechanisms to draw meaningful affinities between inputs and outputs. Due to the configuration of its topology and hyperparameter tuning, it can emulate human-like language by extracting features and learning complex mappings. Atinuke is modular, extensible, and integrates seamlessly with existing machine learning pipelines. Advanced matrix operations like softmax, embeddings, and multi-head attention enable nuanced handling of textual, acoustic, and visual signals. By unifying modern deep learning techniques with software design principles and mathematical theory, the system achieves state-of-the-art results on natural language tasks whilst remaining interpretable and robust. Subjects: Computation and Language (cs.CL) ; Computers and Society (cs.CY); Machine Learning (cs.LG); Software Engineering (cs.SE) MSC classes: I.2.7 ACM classes: I.2.7 Cite as: arXiv:2401.16736 [cs.CL] (or arXiv:2401.16736v1 [cs.CL] for this version) https://doi.org/10.13140/RG.2.2.28532.73600%3B https://doi.org/10.5281/zenodo.10581937 Related DOI: Focus to learn more DOI(s) linking to related resources Submission history From: Abiodun Finbarrs Oketunji [view email] [v1] Tue, 30 Jan 2024 04:29:48 UTC (110 KB) Full-text links: Access Paper: Download a PDF of the paper titled Engineering A Large Language Model From Scratch, by Abiodun Finbarrs Oketunji * Download PDF * Other Formats view license Current browse context: cs.CL < prev | next > new | recent | 2401 Change to browse by: cs cs.CY cs.LG cs.SE References & Citations * NASA ADS * Google Scholar * Semantic Scholar a export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Core recommender toggle CORE Recommender (What is CORE?) About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?) * About * Help * Click here to contact arXiv Contact * Click here to subscribe Subscribe * Copyright * Privacy Policy * Web Accessibility Assistance * arXiv Operational Status Get status notifications via email or slack

Research Papers: transformer architecture, published 2024-2025

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