# MyLLM: A Complete Open-Source Framework for Building Large Language Models from Scratch

> MyLLM is an open-source project for building large language models from scratch, providing a complete pipeline from tokenizer training to RLHF reinforcement learning, helping developers deeply understand every detail of the Transformer architecture.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-03T04:40:00.000Z
- 最近活动: 2026-05-03T04:47:47.977Z
- 热度: 163.9
- 关键词: 大语言模型, Transformer, PyTorch, 开源框架, 机器学习, 深度学习, LLM训练, RLHF, LoRA, GitHub
- 页面链接: https://www.zingnex.cn/en/forum/thread/myllm
- Canonical: https://www.zingnex.cn/forum/thread/myllm
- Markdown 来源: floors_fallback

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## Introduction / Main Post: MyLLM: A Complete Open-Source Framework for Building Large Language Models from Scratch

MyLLM is an open-source project for building large language models from scratch, providing a complete pipeline from tokenizer training to RLHF reinforcement learning, helping developers deeply understand every detail of the Transformer architecture.

## Project Background and Motivation

In the current large language model ecosystem, frameworks like Hugging Face, PyTorch Lightning, and TRL are quite mature, but they encapsulate a lot of low-level details for ease of use. For researchers and developers who want to truly understand the working principles of Transformers, these "black box" abstractions have instead become learning barriers.

The MyLLM project was born out of this need, with its core philosophy being **"From Zero to Hero"**—allowing users to truly understand the complete technology stack of modern large language models by implementing each component with their own hands. This project is not just a framework, but also a systematic learning path.

## Architecture Design: Transparent Technology Stack

MyLLM adopts a layered architecture design, breaking down the complex large model training process into clear and readable modules:

## Core Module Composition

- **model.py**: Defines the core model structure in GPT/LLaMA style
- **api.py**: Provides the LLM class, supporting functions like loading, text generation, batch generation, etc.
- **Configs/**: Uses dataclass to define ModelConfig and GenerationConfig
- **Tokenizers/**: Supports GPT2, LLaMA2, LLaMA3, and trainable tokenizers
- **Train/**: Contains training engines like SFT, DPO, PPO
- **utils/**: Loaders, samplers, weight mappers, and model registries

## Training Engine Architecture

The training module uses a plug-in design and supports multiple training paradigms:

- **SFTTrainer**: Supervised Fine-Tuning Trainer (fully implemented)
- **DPOTrainer**: Direct Preference Optimization (reserved in the framework)
- **PPOTrainer**: Proximal Policy Optimization/RLHF (reserved in the framework)
- **Accelerator**: Supports multiple acceleration schemes like single GPU, DDP, DeepSpeed, FSDP

## Learning Path: From Theory to Practice

MyLLM provides three progressive learning paths to meet the needs of users at different stages:

## 1. Guided Notebooks (notebooks/)

Contains 21 carefully designed Jupyter Notebooks covering every step from word embedding to attention mechanisms, and then to complete model training. Each notebook is equipped with detailed theoretical explanations and runnable code examples.

## 2. Independent Experiment Modules (Modules/)

Breaks down complex concepts into independent experimental units, each module focusing on one core concept such as positional encoding, multi-head attention, layer normalization, etc. This "master one concept at a time" design reduces the learning curve.