README.md

---
license: creativeml-openrail-m
base_model: google/gemma-2b
model-index:
- name: Octopus-V2-2B
  results: []
tags:
- function calling
- on-device language model
- android
inference: false
space: false
spaces: false
language:
- en
metrics:
- accuracy
library_name: adapter-transformers
pipeline_tag: token-classification
---
# Octopus V2: On-device language model for super agent

We are a very small team with many work. Please give us more time to prepare the code, and we will **open source** it. We hope Octopus v2 model will be helpful for you. Let's democratize AI agents for everyone. We've received many requests from car industry, health care, financial system etc. Octopus model is able to be applied to **any function**, and you can start to think about it now.
<p align="center">
- <a href="https://www.nexa4ai.com/" target="_blank">Nexa AI Product</a>
- <a href="https://arxiv.org/abs/2404.01744" target="_blank">ArXiv</a>
- <a href="https://www.youtube.com/watch?v=jhM0D0OObOw&ab_channel=NexaAI" target="_blank">Video Demo</a>
</p>

<p align="center" width="100%">
  <a><img src="Octopus-logo.jpeg" alt="nexa-octopus" style="width: 40%; min-width: 300px; display: block; margin: auto;"></a>
</p>

## Introducing Octopus-V2-2B

Octopus-V2-2B, an advanced open-source language model with 2 billion parameters, represents Nexa AI's research breakthrough in the application of large language models (LLMs) for function calling, specifically tailored for Android APIs. Unlike Retrieval-Augmented Generation (RAG) methods, which require detailed descriptions of potential function arguments—sometimes needing up to tens of thousands of input tokens—Octopus-V2-2B introduces a unique **functional token** strategy for both its training and inference stages. This approach not only allows it to achieve performance levels comparable to GPT-4 but also significantly enhances its inference speed beyond that of RAG-based methods, making it especially beneficial for edge computing devices.

📱 **On-device Applications**:  Octopus-V2-2B is engineered to operate seamlessly on Android devices, extending its utility across a wide range of applications, from Android system management to the orchestration of multiple devices. 

🚀 **Inference Speed**: When benchmarked, Octopus-V2-2B demonstrates a remarkable inference speed, outperforming the combination of "Llama7B + RAG solution" by a factor of 36X on a single A100 GPU. Furthermore, compared to GPT-4-turbo (gpt-4-0125-preview), which relies on clusters A100/H100 GPUs, Octopus-V2-2B is 168% faster. This efficiency is attributed to our **functional token** design.

🐙 **Accuracy**: Octopus-V2-2B not only excels in speed but also in accuracy, surpassing the "Llama7B + RAG solution" in function call accuracy by 31%. It achieves a function call accuracy comparable to GPT-4 and RAG + GPT-3.5, with scores ranging between 98% and 100% across benchmark datasets.

💪 **Function Calling Capabilities**: Octopus-V2-2B is capable of generating individual, nested, and parallel function calls across a variety of complex scenarios.

## Example Use Cases


<p align="center" width="100%">
<a><img src="tool-usage-compressed.png" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
</p>

You can run the model on a GPU using the following code. 
```python
from transformers import AutoTokenizer, GemmaForCausalLM
import torch
import time

def inference(input_text):
    start_time = time.time()
    input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
    input_length = input_ids["input_ids"].shape[1]
    outputs = model.generate(
        input_ids=input_ids["input_ids"], 
        max_length=1024,
        do_sample=False)
    generated_sequence = outputs[:, input_length:].tolist()
    res = tokenizer.decode(generated_sequence[0])
    end_time = time.time()
    return {"output": res, "latency": end_time - start_time}

model_id = "NexaAIDev/Octopus-v2"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = GemmaForCausalLM.from_pretrained(
    model_id, torch_dtype=torch.bfloat16, device_map="auto"
)

input_text = "Take a selfie for me with front camera"
nexa_query = f"Below is the query from the users, please call the correct function and generate the parameters to call the function.\n\nQuery: {input_text} \n\nResponse:"
start_time = time.time()
print("nexa model result:\n", inference(nexa_query))
print("latency:", time.time() - start_time," s")
```

## Evaluation

The benchmark result can be viewed in [this excel](android_benchmark.xlsx), which is manually verified. All the queries in the benchmark test are sampled by Gemini. 

<p align="center" width="100%">
<a><img src="latency_plot.jpg" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto; margin-bottom: 20px;"></a>
<a><img src="accuracy_plot.jpg" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
</p>

**Note**: One can notice that the query includes all necessary parameters used for a function. It is expected that query includes all parameters during inference as well.

## Training Data
We wrote 20 Android API descriptions to used to train the models, see [this file](android_functions.txt) for details. The Android API implementations for our demos, and our training data will be published later. Below is one Android API description example
```
def get_trending_news(category=None, region='US', language='en', max_results=5):
    """
    Fetches trending news articles based on category, region, and language.

    Parameters:
    - category (str, optional): News category to filter by, by default use None for all categories. Optional to provide.
    - region (str, optional): ISO 3166-1 alpha-2 country code for region-specific news, by default, uses 'US'. Optional to provide.
    - language (str, optional): ISO 639-1 language code for article language, by default uses 'en'. Optional to provide.
    - max_results (int, optional): Maximum number of articles to return, by default, uses 5. Optional to provide.

    Returns:
    - list[str]: A list of strings, each representing an article. Each string contains the article's heading and URL.
    """
```


## License
This model was trained on commercially viable data. 


## References
We thank the Google Gemma team for their amazing models!
```
@misc{gemma-2023-open-models,
  author = {{Gemma Team, Google DeepMind}},
  title = {Gemma: Open Models Based on Gemini Research and Technology},
  url = {https://goo.gle/GemmaReport},  
  year = {2023},
}
```

## Citation
```
@misc{chen2024octopus,
      title={Octopus v2: On-device language model for super agent}, 
      author={Wei Chen and Zhiyuan Li},
      year={2024},
      eprint={2404.01744},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```

## Contact
Please [contact us](mailto:[email protected]) to reach out for any issues and comments!