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TLDR
Pioneer Agent is a closed loop system that automates the life cycle of small language models, allowing for autonomous self-improvement. The system identifies usage with AI, finds problems or areas to optimize, and proposes and implements changes automatically. This technology enables anyone to fine-tune models without needing label data or technical expertise.
Key points
- Pioneer Agent automates the life cycle of small language models
- The system identifies usage with AI and finds areas to optimize
- Pioneer Agent proposes and implements changes automatically
- The technology enables fine-tuning of models without label data or technical expertise
- Small fine-tuned models can perform better than frontier models at a fraction of the price
- Pioneer Agent allows for deployment and post-deployment optimization of open-source models
Tools mentioned
Techniques
- fine-tuning
- autonomous self-improvement
- post-deployment optimization
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Transcript (captions)
AI can now autonomously self-improve. That is the premise of the research paper that just dropped out of Fastino Labs. So, if you're not familiar, I'm talking about small language models. That is AI that can run on your computer, your laptop, your phone. These are open-source models meant to be run on device.
And open source models like these are awesome because you can also customize them, also known as fine-tuning. But fine-tuning is kind of difficult even for somebody like me who is quite technical. But now we present Pioneer Agent, a closed loop system that automates this life cycle autonomously. Their system will identify your usage with AI, find any problems or areas to optimize and will propose and do it automatically. This is very much in line with Andre Karpathy's auto research.
And the results are obvious. Look at this. Here are different benchmarks with different open- source models. And as you can see, the dark gray is the base model and the blue is the trained fine-tuned version of that model. And look at the performance increase that it's able to get.
This is the future. But now you don't have to worry about all the complexities of actually doing it yourself. And Fesino is sponsoring this video. And I'm so glad because now I get to tell you about how they optimize the models for you. It allows anybody, even non-technical people, to automatically fine-tune models.
And again, that just means taking a model and making it super good at the use cases you care about. You don't need any label data to start with. Just start using the model and it will improve on its own. And on very specific tasks, you can often get these small fine-tuned models to perform better than the frontier models that you're used to out of the big companies and often at a fraction of the price. You can click to deploy any open- source model and start fine-tuning on it in under 30 seconds.
Post deployment optimization, literally everything for production AI. You can even use Opus and GBT through Pioneer. This is some of the coolest work that I've seen lately on open source models and fine-tuning. This research paper is fascinating. I'm going to drop all the links down below.