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--- |
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license: apache-2.0 |
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base_model: google/gemma-2b |
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model-index: |
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- name: Octopus-V2-2B |
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results: [] |
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tags: |
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- function calling |
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- on-device language model |
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- android |
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inference: false |
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space: false |
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spaces: false |
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language: |
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- en |
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--- |
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# Octopus V2: On-device language model for super agent |
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<p align="center"> |
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- <a href="https://www.nexa4ai.com/" target="_blank">Nexa AI Product</a> |
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- <a href="https://arxiv.org/abs/2404.01744" target="_blank">ArXiv</a> |
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- <a href="https://www.youtube.com/watch?v=jhM0D0OObOw&ab_channel=NexaAI" target="_blank">Video Demo</a> |
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</p> |
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<p align="center" width="100%"> |
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<a><img src="Octopus-logo.jpeg" alt="nexa-octopus" style="width: 40%; min-width: 300px; display: block; margin: auto;"></a> |
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</p> |
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## Introducing Octopus-V2-2B |
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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. |
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📱 **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. Further demonstrations of its capabilities are available on the [Nexa AI Research Page](https://nexaai.github.io/octopus), showcasing its adaptability and potential for on-device integration. |
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🚀 **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. |
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🐙 **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. |
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💪 **Function Calling Capabilities**: Octopus-V2-2B is capable of generating individual, nested, and parallel function calls across a variety of complex scenarios. |
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## Example Use Cases |
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<p align="center" width="100%"> |
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<a><img src="tool-usage-compressed.png" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a> |
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</p> |
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You can run the model on a GPU using the following code. |
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```python |
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from transformers import AutoTokenizer, GemmaForCausalLM |
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import torch |
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import time |
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def inference(input_text): |
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start_time = time.time() |
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input_ids = tokenizer(input_text, return_tensors="pt").to(model.device) |
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input_length = input_ids["input_ids"].shape[1] |
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outputs = model.generate( |
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input_ids=input_ids["input_ids"], |
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max_length=1024, |
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do_sample=False) |
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generated_sequence = outputs[:, input_length:].tolist() |
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res = tokenizer.decode(generated_sequence[0]) |
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end_time = time.time() |
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return {"output": res, "latency": end_time - start_time} |
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model_id = "NexaAIDev/android_API_10k_data" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = GemmaForCausalLM.from_pretrained( |
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model_id, torch_dtype=torch.bfloat16, device_map="auto" |
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) |
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input_text = "Take a selfie for me with front camera" |
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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:" |
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start_time = time.time() |
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print("nexa model result:\n", inference(nexa_query)) |
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print("latency:", time.time() - start_time," s") |
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``` |
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## Evaluation |
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<p align="center" width="100%"> |
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<a><img src="latency_plot.jpg" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto; margin-bottom: 20px;"></a> |
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<a><img src="accuracy_plot.jpg" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a> |
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</p> |
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## Training Data |
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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 |
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``` |
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def get_trending_news(category=None, region='US', language='en', max_results=5): |
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""" |
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Fetches trending news articles based on category, region, and language. |
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Parameters: |
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- category (str, optional): News category to filter by, by default use None for all categories. Optional to provide. |
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- region (str, optional): ISO 3166-1 alpha-2 country code for region-specific news, by default, uses 'US'. Optional to provide. |
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- language (str, optional): ISO 639-1 language code for article language, by default uses 'en'. Optional to provide. |
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- max_results (int, optional): Maximum number of articles to return, by default, uses 5. Optional to provide. |
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Returns: |
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- list[str]: A list of strings, each representing an article. Each string contains the article's heading and URL. |
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""" |
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``` |
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## License |
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This model was trained on commercially viable data and is under the [Nexa AI community disclaimer](https://www.nexa4ai.com/disclaimer). |
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## References |
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We thank the Google Gemma team for their amazing models! |
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``` |
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@misc{gemma-2023-open-models, |
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author = {{Gemma Team, Google DeepMind}}, |
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title = {Gemma: Open Models Based on Gemini Research and Technology}, |
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url = {https://goo.gle/GemmaReport}, |
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year = {2023}, |
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} |
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``` |
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## Citation |
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``` |
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@misc{chen2024octopus, |
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title={Octopus v2: On-device language model for super agent}, |
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author={Wei Chen and Zhiyuan Li}, |
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year={2024}, |
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eprint={2404.01744}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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## Contact |
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Please [contact us](mailto:alexchen@nexa4ai.com) to reach out for any issues and comments! |
