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- This open-source project aims to train a small-parameter, visually-capable language model + MiniMind-V from scratch, with the goal of achieving this in as little as 3 hours. + +
- MiniMind-V is also extremely lightweight, with the smallest version being approximately + 1/7000 the size of GPT3, striving to enable quick inference and even training on personal GPUs. + +
- MiniMind-V provides the full-stage code for a simplified large model structure, dataset + cleaning and preprocessing, supervised pretraining, supervised instruction fine-tuning (SFT). + It also includes code for expanding to sparse models with mixed experts (MoE). + +
- This is not just an implementation of an open-source model; it is also a tutorial for beginners to enter the + field of Vision-Language Models (VLM). + +
- We hope this project can serve as a starting point for researchers, providing an introductory example that + helps everyone quickly get started and inspires more exploration and innovation in the VLM domain. + +
- To prevent misinterpretation, "from scratch" specifically refers to building upon the + pure language model MiniMind (which is a GPT-like model trained entirely from scratch) to further expand its + capabilities from 0 to 1 in terms of visual abilities. + For detailed information on the latter, please refer to the twin project MiniMind. + + +
- To avoid misinterpretation, "fastest 3 hours" means you need a machine with hardware + configuration superior + to mine. Detailed specifications will be provided below. + +
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