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Tutorial on implementing a diffusion model based on Google's Gemma architecture from scratch.
This article kicks off a series on building a debugger from scratch in Rust, starting with attaching to a Windows process using OS debugging APIs.
A GitHub repository implementing fundamental machine learning algorithms from scratch using plain NumPy, designed to help learners understand the inner workings of algorithms by focusing on clarity over performance. It covers supervised, unsupervised, deep learning, and reinforcement learning topics.
A 3-hour hands-on walkthrough of building an LLM from scratch, covering five stages: LLM basics, Transformer architecture, training, model modernization, and scaling, with code visible throughout.
A 270M parameter language model trained from scratch on English Wikipedia and instruction-tuned for conversational AI, developed as an independent research project.
Sebastian Raschka announces the release of his book 'Build a Reasoning Model (From Scratch)' after 18 months of work, covering inference scaling, reinforcement learning, and distillation from scratch.
A blog post tutorial on building AI agents from scratch in Python, seeking community feedback.
Recommended a GitHub repo for building a GPT-style Transformer from scratch without advanced libraries. With 13M parameters, it can be trained in one day on free Colab to generate grammatically correct text.
NanoEuler is a GPT-2-scale language model built entirely from scratch in C/CUDA without any ML libraries, including hand-written forward/backward passes, a byte-level BPE tokenizer, and training pipeline. The project is an educational artifact demonstrating the engineering behind transformer training and runs on a single RTX 4070.
A Twitter thread discusses the irony of Andrej Karpathy's teaching philosophy—learning by building from scratch—while he now uses AI to code, and shares a 3-week learning journey using Claude Opus 4.8 to master deep learning the old way.
A developer built a transformer model entirely from scratch in TypeScript, including a custom autograd engine, and released it as an open-source educational tool on GitHub.
A guide on building a large language model from scratch using Apple's MLX framework.
Stanford CS336 aims to teach students how to build language models from scratch, with deep understanding of the full-stack design of data, systems, and models. The course videos are publicly available and suitable for AI beginners.
The author details the process of pretraining and post-training a 500M parameter language model and a 330M parameter image generator entirely from scratch.
A GitHub open-source project that implements the complete GPT training pipeline from scratch, including data preprocessing, pretraining, SFT, and RLHF post-training, all based on native PyTorch. Ideal for developers who want to deeply understand the Transformer architecture.
A repository that builds a transformer from scratch without high-level libraries, explaining attention mechanisms and the full training pipeline, trainable in a day on free Colab.
A repository that builds a GPT-style transformer from scratch without high-level libraries, covering everything from data preprocessing to generation, and includes guides for SFT and RLHF.
The author documents their journey of building a 340M parameter LLM from scratch, trained exclusively on pre-1900 texts, including custom datasets, training scripts, and open-sourcing the model and code.
Fable 5 demonstrates its ability to model the entire solar system from scratch and accurately predict the timing of a solar eclipse.
The author built Joey, a 170M parameter masked diffusion language model from scratch, trained on FineWeb-Edu and fine-tuned on DailyDialog, achieving fluent but incoherent sentences due to capacity limitations. The project highlights the differences from autoregressive LLMs and the lessons learned from building and debugging the system.