---
license: cc-by-nc-4.0
---
# Octopus Planner: On-device Language Model for AI Agent Framework

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.

### Key Features of Octopus Planner:
- **Efficient Planning**: Utilizes Phi-3 Mini architecture with only 3.8 billion parameters for high efficiency and low power consumption.
- **Enhanced Accuracy**: Achieves a planning success rate of 97%, providing reliable and effective performance.
- **On-device Operation**: Designed for edge devices, ensuring fast response times and enhanced privacy by processing data locally.
- **Cost-Effective**: Reduces operational costs by minimizing the key-value cache required, which also improves battery life.
- **Fine-tuned**: Extensively fine-tuned on specialized tasks to ensure high accuracy and contextual understanding.

## Innovative Framework
The Octopus Planner introduces a specialized Planner and Action Agents Framework:
- **Dual Model Architecture**: Separates planning and action, allowing for specialized optimization and improved scalability.
- **Focused Training**: Employs fine-tuning over traditional long prompting, improving efficiency without sacrificing accuracy.
- **Comprehensive Benchmarking**: Includes rigorous in-domain testing to validate the model's effectiveness in real-world scenarios.

## Example Usage
Below is a code snippet to use Octopus Planner for scheduling a team meeting:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "nexa-collaboration/planning-phi3-128k-full"
tokenizer_id = "microsoft/Phi-3-mini-128k-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)

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"

inputs = f"<|user|>{question}<|end|><|assistant|>"
input_ids = tokenizer(inputs, return_tensors="pt").to(model.device)

outputs = model.generate(
        input_ids=input_ids["input_ids"], 
        max_length=1024,
        do_sample=False)

res = tokenizer.decode(outputs.tolist()[0])
print(f"=== inference result ===\n{res}")
```

## Training Data
We wrote 10 Android API descriptions to used to train the models, see this file for details. Below is one Android API description example
```
def send_email(recipient, title, content):
    """
    Sends an email to a specified recipient with a given title and content.

    Parameters:
    - recipient (str): The email address of the recipient.
    - title (str): The subject line of the email. This is a brief summary or title of the email's purpose or content.
    - 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.
    """
```

## Contact Us
For support or to provide feedback, please [contact us](mailto:octopus@nexa4ai.com).

## License and Citation
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.
```
@article{nexa2024octopusplanner,
  title={Planner-Action Agents Framework for On-device Small Language Models},
  author={Nexa AI Team},
  journal={ArXiv},
  year={2024},
  volume={abs/2404.11459}
}
```