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+# 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}
+}
+```