Octo-planner-2b
| Property | Value |
|---|---|
| Parameter Count | 2.51 Billion |
| Base Model | Gemma-2b |
| Developer | NexaAIDev |
| Paper | arXiv:2406.18082 |
| Model URL | HuggingFace |
What is octo-planner-2b?
Octo-planner-2b is an innovative on-device language model specifically designed for the Planner-Action Agents Framework. Built on Google's Gemma-2b architecture, it represents a significant advancement in edge AI computing, enabling efficient planning capabilities without requiring cloud connectivity. The model achieves an impressive 98.1% planning success rate on benchmark datasets while maintaining low power consumption.
Implementation Details
The model is implemented using the Transformers library and can be easily deployed using PyTorch. It supports bfloat16 precision for optimal performance and includes specialized optimizations for edge devices. The model processes input through a structured format using specific tokens like <|user|> and <|assistant|> for clear interaction patterns.
- Built on Gemma-2b architecture with 2.51B parameters
- Optimized for edge device deployment
- Supports local processing without cloud dependency
- Integrates with the Planner-Action Agents Framework
Core Capabilities
- High-efficiency planning with 98.1% success rate
- Local processing for enhanced privacy and reduced latency
- Specialized API integration for Android systems
- Optimized for low power consumption on edge devices
- Seamless integration with Octopus-V2 for comprehensive agent capabilities
Frequently Asked Questions
Q: What makes this model unique?
Octo-planner-2b stands out for its ability to perform complex planning tasks entirely on-device while maintaining high accuracy. Its specialized optimization for the Planner-Action Agents Framework and integration capabilities with Android APIs make it particularly valuable for edge computing applications.
Q: What are the recommended use cases?
The model is ideal for applications requiring local planning capabilities, such as personal assistants, IoT device coordination, and automated task scheduling. It's particularly well-suited for scenarios where privacy, low latency, and offline operation are crucial.
