Petals: Torrent GPT, A decentralized AI

Large language models (LLMs) have become increasingly popular in recent years, due to their ability to perform a wide range of tasks, such as text generation, translation, and question answering. However, LLMs can be computationally expensive to run, which can limit their accessibility.

To address this challenge, Yandex Research has developed Petals, a decentralized system for LLM inference and finetuning. Petals works by splitting an LLM into several blocks, which are then distributed across a network of servers. This allows Petals to scale to large models, while still being efficient.

In addition to inference, Petals can also be used to finetune LLMs. This means that Petals can be used to adapt an LLM to a specific task or domain. To do this, Petals uses a process called federated learning, which allows LLMs to be trained on data that is distributed across a network of devices.

Petals is an open-source system, which means that it can be used by anyone. This makes Petals a valuable tool for researchers, developers, and businesses that want to use LLMs.

 

Benefits of Petals

There are several benefits to using Petals for LLM inference and finetuning:

Scalability: Petals can scale to large models, while still being efficient. This makes it a good choice for tasks that require a lot of computing power, such as text generation and translation.

Flexibility: Petals is a decentralized system, which means that it can be used with a variety of hardware configurations. This makes it a good choice for businesses and organizations that want to use LLMs, but do not have the resources to set up a centralized infrastructure.

Open-source: Petals is an open-source system, which means that it can be used by anyone. This makes it a valuable tool for researchers and developers who want to experiment with LLMs.

How to Use Petals

To use Petals, you will need to install the Petals software. You can do this by following the instructions 

pip install -U petals
python -m petals.cli.run_server bigscience/bloom

Once you have installed the software, you can start using Petals to inference or finetune LLMs.

To inference an LLM, you will need to provide Petals with the model's parameters and the input data. Petals will then distribute the inference task across the network of servers and return the results.

To finetune an LLM, you will need to provide Petals with the model's parameters, the input data, and the desired output. Petals will then use federated learning to train the model on the data.

Here is an example on how to use Petals :

Current Status

Petals is currently under development, but it is already a valuable tool for researchers and developers who want to experiment with LLMs. The Petals team is working on improving the performance of Petals, as well as adding new features. Petals Github repository can be found here : https://github.com/bigscience-workshop/petals

 

Future Plans

The Petals team plans to continue developing Petals and making it more accessible to a wider audience. They also plan to add new features to Petals, such as the ability to train LLMs on images and videos.

Original post can be found here: Petals: decentralized inference and finetuning of large language models (yandex.com)

Conclusion

Petals is a powerful tool for LLM inference and finetuning. It is scalable, flexible, and open-source, which makes it a good choice for a wide range of users. If you are looking for a way to use LLMs, I encourage you to try Petals.

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