Experimenting with an event-driven 1B architecture on consumer hardware
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OpenAI 相关模型动态已经出现，适合跟踪能力变化、生态影响和后续可用性。
一位开发者正在尝试一种事件驱动的1B架构，具体来说是一个1B参数的RHEA模型。他们已经完成了内存和计算优化，使得这个模型能够在消费级硬件上运行。目前，该模型可以在配备了RTX 5070笔记本电脑GPU（拥有8 GB显存）的笔记本电脑上进行实验，这表明在更易获得的系统上运行大型模型是可行的。
I’m a solo developer experimenting with alternative neural network architectures, and I wanted to share a project I’ve been working on recently.
For some time I’ve been exploring SSN-inspired models and event-driven computation. My latest experiment is an architecture I call RHEA.
RHEA is built around event-driven responses rather than trying to reproduce a conventional Transformer stack. The architecture does not rely on the standard attention/MLP block structure used by most Transformer language models, and I’m experimenting with a different internal processing approach.
One of my main goals is to make training relatively large experimental models possible on consumer hardware.
I’ve also been working on memory and compute optimizations that currently allow me to experiment with a roughly 1B-parameter RHEA model on a laptop with an RTX 5070 Laptop GPU with 8 GB of VRAM.
That hardware limitation is actually part of the experiment: I’m interested in seeing how far this type of architecture can be pushed without access to large GPU clusters.
It’s still very much a research project, so I’m not claiming that it replaces Transformers or that the architecture is already proven. Right now I’m mainly interested in training behavior, efficiency, scaling characteristics, and how the event-driven approach behaves as the model gets larger.
I’d be interested to hear what people working on alternative architectures or efficient training think about this direction.
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https://preview.redd.it/ewmjr0zfz7lh1.png?width=1080&format=png&auto=webp&s=bb91aa193608bb652eceae480a2cd1bb29a1599a