Petals

About Petals

Petals: Collaborative Language Model Execution

Petals is a powerful tool designed to run large language models, such as BLOOM-176B, collaboratively by loading a small part of the model and teaming up with others serving the remaining parts. This innovative approach enables efficient inference and fine-tuning, making it suitable for chatbots and other interactive applications.

Key Features of Petals

Petals provides several advantages over classic language model APIs:

• Collaborative Execution: Run large language models by sharing their parts among multiple users
• Fast Inference: Achieve single-batch inference at ≈ 1 sec per step (token), up to 10x faster than offloading
• Parallel Inference: Reach hundreds of tokens per second for efficient processing
• PyTorch Flexibility: Leverage fine-tuning, sampling methods, custom paths, and access to hidden states

Benefits of Using Petals

With Petals, users can experience:

• Efficiency: Collaboratively run large language models for improved performance and reduced resource consumption
• Speed: Execute single-batch inference quickly for interactive applications like chatbots
• Flexibility: Access the benefits of an API combined with the adaptability of PyTorch
• Collaboration: Contribute to a public swarm or create a private swarm with trusted individuals

Applications of Petals

Petals is suitable for a wide range of use cases, including:

• Chatbots: Build and deploy fast, interactive chatbots with large language models
• Text Generation: Generate high-quality text for creative writing, content generation, or summarization
• Sentiment Analysis: Run advanced sentiment analysis on large datasets
• Translation: Leverage large language models for accurate translation services

Summary

Petals offers an innovative approach to running large language models collaboratively and efficiently. By combining the power of multiple users, Petals unlocks the full potential of models like BLOOM-176B for various applications. Experience the flexibility of PyTorch and the performance benefits of collaborative execution with Petals.