TorrentGTP (Petals)
The Petals Revolution: Decentralizing AI Through Torrent Technology
Introduction
What could be the biggest advancement in artificial intelligence since the Transformer model? Imagine running even the largest AI models on any device at impressive speeds. The best part? It's based on technology that has existed for decades. Let's explore what this innovation is, why it matters, and how you can start using it today.
The Current State of Large Language Models
For those who might not be following recent AI developments, Large Language Models (LLMs) are the technology powering ChatGPT. These models represent a significant step toward artificial general intelligence and have already demonstrated tremendous value to society.
However, ChatGPT, the most prominent model, comes with limitations:
- It's centralized and closed-source
- This creates problems with privacy, security, cost, and transparency
After ChatGPT's success, open-source models began appearing - Llama, Bloom, MPT, and others. These models offered several advantages:
- Free to use
- Transparent
- Open-source
- Relatively easy to install
But they come with one major drawback: they require modern, often expensive hardware. Running a mid-sized 33 billion parameter model typically needs a GPU costing $1,000+ to achieve decent inference speeds - putting this technology out of reach for most people globally.
The AI Access Dilemma
Until recently, if you wanted to experiment with large language models, you had just two options:
1. Use a third-party proprietary API like ChatGPT
2. Invest in powerful (and expensive) GPU hardware for your computer
Enter Petals: The Torrent Approach to AI
Now there's a third option: Petals - a decentralized method for running and fine-tuning large language models.
Remember how torrents revolutionized file sharing? Petals applies similar principles to AI. Each model is broken down into small blocks and distributed across ordinary computers worldwide. You don't need mainframe computers - just regular consumer-grade devices, each storing only small pieces of the model.
As with torrents, the network benefits from having many contributors. By storing a small piece of a model on your computer, combined with contributions from others worldwide, you create what amounts to one of the most powerful AI computing networks in existence.
You can even run this on Google Colab's free tier, eliminating the need for local hardware. And because it functions like a torrent, the network improves as more people join and contribute.
Performance Metrics
Currently, Petals achieves 5-6 tokens per second on the Llama 65 billion parameter model - remarkable considering even top consumer graphics cards struggle to run this model directly. The system offers varying speeds depending on model size and server configurations.
How to Join the Network
You can participate in three ways:
1. As a client: Using the network to train or run models
2. As a server: Providing your hardware to help run models
3. As both client and server
For clients, the process is simple - just run some Python code from the Petals library. No need to worry about server architecture or network health.
Setting up a server is equally straightforward with just a few lines of Python code. You can even create private swarms for specific applications.
Future Potential
What's particularly exciting is where this technology could lead. Since everything is distributed and model components aren't necessarily directly dependent on each other, this approach might be especially suitable for mixture of experts architecture - supposedly how GPT-4 achieves such impressive quality using eight separate trained models working together.
The Challenge of Resource Donation
The main limitation of this distributed model is its reliance on people donating idle computing resources. It depends on users sharing GPU time that would otherwise go unused.
Incentivizing Contribution
How do you motivate people to donate their idle GPU time? An obvious approach would be to reward contributors with tokens for their compute power.
This could be implemented in various ways, such as giving contributing servers rewards that provide priority access or higher usage allowances on the network. These tokens could potentially be exchanged for monetary value, creating a strong incentive for network contribution.
Current Support and Implementation
Petals currently supports the Bloom and Llama models - the primary open-source options available. Implementation couldn't be simpler:
```python
# With just a few lines of code for inference and fine-tuning
# Import from the petals library
from petals import inference, training
# Generate a response
response = inference.generate("Your prompt here")
```
This straightforward approach makes powerful AI more accessible than ever before.
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