Breakthrough: Run Massive Models On Any Device (ex: LLaMA 65b)

Introduction to Pedals: Decentralized AI

In this section, the speaker introduces Pedals, a decentralized method of running and fine-tuning large language models. The speaker explains the significance of this advancement in artificial intelligence and highlights the challenges faced with existing models.

Advancement in Artificial Intelligence

• This could be the biggest advancement in artificial intelligence since the Transformers model.

• Pedals allows running large language models on any device at fast speeds.

• It is based on a technology that has been around for decades.

Large Language Models and Challenges

• Large language models, such as Chachi PT, are an incredible step forward in reaching artificial general intelligence.

• Chachi PT is centralized and closed source, posing privacy, security, cost, and transparency issues.

• Open-source models like Llama Bloom MPT have been released but require modern and expensive hardware to run effectively.

Introducing Pedals - Decentralized AI

• Pedals is a decentralized method of running and fine-tuning large language models.

• It utilizes a torrent-like network where models are broken down into blocks stored on individual computers worldwide.

• Contributions from multiple users create a powerful AI network without requiring massive mainframe computers.

• Users can even run Pedals on the free tier of Google Colab.

Benefits and Speeds Achieved by Pedals

• Pedals achieves high speeds with different models and server configurations.

• It currently achieves five to six tokens per second on the Llama 65 billion parameter model.

• This surpasses what consumer graphics cards can directly handle.

Using Pedals as a Client or Server

As a Client

• Clients use the network to train or run their models.

• No need to worry about server architecture or swarm health.

• Running Python code from the Pedals library is all that's required.

As a Server

• Servers provide their hardware to help run models on the network.

• Setting up a server is simple with just a few lines of Python code.

• Private swarms can also be created.

Potential for Distributed Models and Torrent Computing

• Distributed models and torrent computing have potential benefits, especially for architectures like the mixture of experts used in GPT E4.

• However, it relies on people donating their idle resources, such as GPU time.

Incentivizing Contribution to the Network

• One way to incentivize contribution is by rewarding compute power using blockchain technology.

• Contributing servers could receive rewards that provide priority or higher usage of the total network.

• These rewards could potentially be traded for monetary value.

Current Support and Ease of Use

• Pedals currently supports Bloom and Llama models, which are open source.

• It is easy to use with just a few lines of code for inference and fine-tuning.

The transcript has been summarized in a clear and concise manner using timestamps when available.