github
/
minimind
mirror of https://github.com/jingyaogong/minimind.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

update readme

website
gongjy 2024-10-09 09:12:24 +08:00
parent ed0880ebae
commit 70c60f5655
1 changed files with 10 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

21
README.md
View File

@ -25,18 +25,17 @@
* This open-source project aims to train a miniature language model **MiniMind** from scratch, with a size of just 26MB.
* **MiniMind** is extremely lightweight, approximately 1/7000 the size of GPT-3, designed to enable fast
inference and even training on CPUs.
* **MiniMind** is an improvement on the DeepSeek-V2 and Llama3 architectures. The project includes all stages of data
processing, pretraining, SFT, and DPO, and features a Mixture of Experts (MoE) model.
* This is not only the implementation of an open-source model, but also a tutorial for getting started with large
language models (LLMs).
* We hope that this project serves as a stepping stone for researchers and developers, providing an introductory example
to help them quickly get started and foster more exploration and innovation in the LLM field.
* This open-source project aims to train a tiny language model called **MiniMind** from scratch in just 3 hours, with a model size of only 26.88M.
> To avoid any misunderstanding, "fastest 3 hours" refers to the requirement of using hardware with higher
specifications than the author's setup. Detailed specifications will be provided below.
* **MiniMind** is extremely lightweight, with the smallest version being approximately $\frac{1}{7000}$ the size of GPT3, making it possible for even an ordinary personal GPU to perform quick inference and even training.
* **MiniMind** provides the full-stage code for a simplified large model structure, dataset cleaning and preprocessing, supervised pretraining, supervised instruction fine-tuning (SFT), low-rank adaptation (LoRA) fine-tuning, and direct preference alignment with reinforcement learning without rewards (DPO). It also includes code for expanding to sparse models with mixed experts (MoE) and multi-modal vision language models (VLM): <a href="https://github.com/jingyaogong/minimind-v">MiniMind-V</a>.
* This is not just an implementation of an open-source model but also a tutorial for getting started with large language models (LLM).
* We hope this project will serve as an introductory example for researchers, helping them quickly get started and inspiring more exploration and innovation in the LLM field.
> To avoid misinterpretation, "fastest 3 hours" means you need a machine with hardware configuration superior to mine. Detailed specifications will be provided below.