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  license: cc-by-nc-4.0
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- # Octopus Planner: On-device Language Model for AI Agent Framework
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  We're thrilled to introduce the Octopus Planner, the latest breakthrough in on-device language models from Nexa AI. Developed for the Planner-Action Agents Framework, Octopus Planner leverages state-of-the-art technology to enhance AI agents' decision-making processes directly on edge devices. By enabling rapid and efficient planning and action execution without the need for cloud connectivity, this model together with [Octopus-V2](https://huggingface.co/NexaAIDev/Octopus-v2) can work on edge devices locally to support AI Agent usages.
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  - **Comprehensive Benchmarking**: Includes rigorous in-domain testing to validate the model's effectiveness in real-world scenarios.
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  ## Example Usage
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- Below is a code snippet to use Octopus Planner for scheduling a team meeting:
 
 
 
 
 
 
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  license: cc-by-nc-4.0
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+ # Octopus Planner: On-device Language Model for Planner-Action Agents Framework
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  We're thrilled to introduce the Octopus Planner, the latest breakthrough in on-device language models from Nexa AI. Developed for the Planner-Action Agents Framework, Octopus Planner leverages state-of-the-art technology to enhance AI agents' decision-making processes directly on edge devices. By enabling rapid and efficient planning and action execution without the need for cloud connectivity, this model together with [Octopus-V2](https://huggingface.co/NexaAIDev/Octopus-v2) can work on edge devices locally to support AI Agent usages.
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  - **Comprehensive Benchmarking**: Includes rigorous in-domain testing to validate the model's effectiveness in real-world scenarios.
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  ## Example Usage
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+ Below is a code snippet to use Octopus Planner:
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+ <p align="center" width="100%">
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+ <a><img src="1-demo.png" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
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+ </p>
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+ Run below code to use Octopus Planner for a given question:
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  ```python
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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