---
license: apache-2.0
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
---
# Octopus V2: On-device language model for super agent
- Nexa AI Product
- ArXiv
- Video Demo
## 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
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.
**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 and is under the [Nexa AI community disclaimer](https://www.nexa4ai.com/disclaimer).
## 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:alexchen@nexa4ai.com) to reach out for any issues and comments!