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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+octopus-planning - AWQ
+- Model creator: https://huggingface.co/NexaAIDev/
+- Original model: https://huggingface.co/NexaAIDev/octopus-planning/
+
+
+
+
+Original model description:
+---
+license: cc-by-nc-4.0
+---
+# Octo-planner: On-device Language Model for Planner-Action Agents Framework
+
+We're thrilled to introduce the Octo-planner, the latest breakthrough in on-device language models from Nexa AI. Developed for the Planner-Action Agents Framework, Octo-planner enables rapid and efficient planning 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 Octo-planner:
+- **Efficient Planning**: Utilizes fine-tuned plan model based on Phi-3 Mini (3.82 billion parameters) for high efficiency and low power consumption.
+- **Agent Framework**: Separates planning and action, allowing for specialized optimization and improved scalability.
+- **Enhanced Accuracy**: Achieves a planning success rate of 98.1% on benchmark dataset, providing reliable and effective performance.
+- **On-device Operation**: Designed for edge devices, ensuring fast response times and enhanced privacy by processing data locally.
+
+
+## Example Usage
+Below is a demo of Octo-planner:
+<p align="center" width="100%">
+<a><img src="1-demo.png" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+
+
+Run below code to use Octopus Planner for a given question:
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "NexaAIDev/octopus-planning"
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(model_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:[email protected]).
+
+## 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{chen2024octoplannerondevicelanguagemodel,
+      title={Octo-planner: On-device Language Model for Planner-Action Agents}, 
+      author={Wei Chen and Zhiyuan Li and Zhen Guo and Yikang Shen},
+      year={2024},
+      eprint={2406.18082},
+      url={https://arxiv.org/abs/2406.18082}, 
+}
+```
+