Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

Learn AI and LLMs from scratch using free resources

仅需Python基础，从0构建大语言模型；从0逐步构建GLM4\Llama3\RWKV6， 深入理解大模型原理

Implement a reasoning LLM in PyTorch from scratch, step by step

LLMs-from-scratch项目中文翻译

Demystify RAG by building it from scratch. Local LLMs, no black boxes - real understanding of embeddings, vector search, retrieval, and context-augmented generation.

An LLM-powered advanced RAG pipeline built from scratch

[ICLR 2025] Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing. Your efficient and high-quality synthetic data generation pipeline!

From-scratch PyTorch implementation of Google's TurboQuant (ICLR 2026) for LLM KV cache compression. 5x compression at 3-bit with 99.5% attention fidelity.

Train a 1B LLM with 1T tokens from scratch by personal

Single File, Single GPU, From Scratch, Efficient, Full Parameter Tuning library for "RL for LLMs"

~950 line, minimal, extensible LLM inference engine built from scratch.

Building LLM-Enabled Multi Agent Applications from Scratch

🤖 State-of-the-art, production ready LLM apps made mega-easy, so you don't have to build them from scratch 🤯 Create a bot, now 🫵

Building a GPT-like LLM from scratch with PyTorch.

A comprehensive Rust translation of the code from Sebastian Raschka's Build an LLM from Scratch book.

Mastering Large Language Models: Build Your Own LLM from Scratch

Building a 2.3M-parameter LLM from scratch with LLaMA 1 architecture.

My Solution and Notes for the Stanford CS336: LLM from scratch

Build a Large Language Model (From Scratch) book and Finetuned Models

nanoRLHF: from-scratch journey into how LLMs and RLHF really work.

a LLM cookbook, for building your own from scratch, all the way from gathering data to training a model

🎤💬 Full example of implementing ChatGPT's realtime voice from scratch with VAD + STT + LLM + TTS technology stack within almost one file!

LLM inference engine from scratch — paged KV cache, continuous batching, chunked prefill, prefix caching, speculative decoding, CUDA graph, tensor parallelism, MoE expert parallelism, OpenAI-compatible serving

Build your own X - Master machine learning by building everything from scratch. It aims to cover everything from linear regression to deep learning to large language models (LLMs).

从零搭建Agent框架(Build LLM ReAct Agent from scratch)

Coding an LLM and its building blocks from scratch.

Tiny C++ LLM inference implementation from scratch

Build LLM from scratch

lm-scratch-pytorch - The code is designed to be beginner-friendly, with a focus on understanding the fundamentals of PyTorch and implementing LLMs from scratch,step by step.