volotat/mini-AGI

选用指南

AI 根据 README 整理 · 2026/10/16 20:11:53

使用方式

模型通过读取语料流进行训练，训练和生成使用相同的前向路径，可通过命令行或脚本运行。

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model. *The name is a half-joke and not a statement of the current capabilities of the model, but rather the potential and traits it has. Continual single-stream learning and natural size and processing adaptation to the available resources exactly what I myself expect from an AGI to have. It has just a small toy-level memory footprint and hence it is “mini-AGI”.*  *Here is how min-run dashboard looks like. The model is pointed to the corpus to constantly read and learn from.*

**mini-AGI** model has small enough GPU footprint that it is possible to train end-to-end on one consumer card, and it is built so that training never has to stop. It reads a stream of characters one chunk at a time, takes a gradient step on each, and the same path serves generation. There is no separate fine-tuning regime and no frozen base: reading and being trained are the same event. Three constraints shape everything else in the design:

部署要求

需要至少 8GB 显存的 GPU，且参数数量受限于磁盘空间而非显存。

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# mini-AGI mini-AGI - is a **continual learning** byte-level language model that assembles its own architecture, trains from scratch on a single 8 GB VRAM GPU, and keeps learning from everything it reads. It stores its weights as ordinary files on disk and pages them onto the card as it needs them, so the parameter count is bounded by free disk space rather than by VRAM. It grows new capacity while training when it runs short, prunes what nothing asks for, and reads through exactly the same code path it serves on. Targeted at a PC or laptop with at least an 8 GB VRAM GPU on the board.

已知限制

作者明确说明当前模型是玩具级（toy-level），不具备前沿能力，且最终权重尚未发布。

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**NOTE: as of now this is a small toy-level model.** Do not expect a frontier level capabilities. This is rather a small experiment to show, that continual learning from the single stream of data without catastrophic forgetting is possible. Furthermore it is possible on a modest hardware. Which means that almost everyone could train their own version of the model (or simply continue training this one) exactly as they see it fit. And the capabilities would be bounded by the actual hardware, scale and quality of the data available and the amount of time one willing to spend on training the

The "final" weights are **not published yet**. The run is still reading its first pass over the corpus, the weights go up once it has been through all of it, which is several weeks away at the current rate. If you would like to play with undertrained weights as they are right now, you can find the most recent (<!-- auto:weights-date -->12 Oct 2026<!-- /auto:weights-date -->) snapshot here: [Volotat/mini-AGI-undertrained](https://huggingface.co/Volotat/mini-AGI-undertrained/tree/main) <!-- auto:run-blocks --> <details> <summary><b>Graph of the whole run so far</b></summary>