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---
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license: cc-by-nc-4.0
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---
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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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### Key Features of Octopus Planner:
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- **Efficient Planning**: Utilizes fine-tuned plan model based on Phi-3 Mini (3.8 billion parameters) for high efficiency and low power consumption.
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- **Agent Framework**: Separates planning and action, allowing for specialized optimization and improved scalability.
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- **Enhanced Accuracy**: Achieves a planning success rate of 97% on benchmark dataset, providing reliable and effective performance.
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- **On-device Operation**: Designed for edge devices, ensuring fast response times and enhanced privacy by processing data locally.
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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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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "NexaAIDev/octopus-planning"
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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question = "Find my presentation for tomorrow's meeting, connect to the conference room projector via Bluetooth, increase the screen brightness, take a screenshot of the final summary slide, and email it to all participants"
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inputs = f"<|user|>{question}<|end|><|assistant|>"
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input_ids = tokenizer(inputs, return_tensors="pt").to(model.device)
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outputs = model.generate(
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input_ids=input_ids["input_ids"],
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max_length=1024,
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do_sample=False)
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res = tokenizer.decode(outputs.tolist()[0])
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print(f"=== inference result ===\n{res}")
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```
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## Training Data
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We wrote 10 Android API descriptions to used to train the models, see this file for details. Below is one Android API description example
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```
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def send_email(recipient, title, content):
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"""
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Sends an email to a specified recipient with a given title and content.
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Parameters:
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- recipient (str): The email address of the recipient.
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- title (str): The subject line of the email. This is a brief summary or title of the email's purpose or content.
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- content (str): The main body text of the email. It contains the primary message, information, or content that is intended to be communicated to the recipient.
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"""
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```
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## Contact Us
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For support or to provide feedback, please [contact us](mailto:octopus@nexa4ai.com).
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## License and Citation
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Refer to our [license page](https://www.nexa4ai.com/licenses/v2) for usage details. Please cite our work using the below reference for any academic or research purposes.
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```
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@article{nexa2024octopusplanner,
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title={Planner-Action Agents Framework for On-device Small Language Models},
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author={Nexa AI Team},
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journal={ArXiv},
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year={2024},
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volume={abs/2404.11459}
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}
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``` |
