NexusRaven is an open-source and commercially viable function calling LLM that surpasses the state-of-the-art in function calling capabilities.
💪 Versatile Function Calling Capability: NexusRaven-V2 is capable of generating single function calls, nested calls, and parallel calls in many challenging cases.
🤓 Fully Explainable: NexusRaven-V2 is capable of generating very detailed explanations for the function calls it generates. This behavior can be turned off, to save tokens during inference.
📊 Performance Highlights: NexusRaven-V2 surpasses GPT-4 by 7% in function calling success rates in human-generated use cases involving nested and composite functions.
🔧 Generalization to the Unseen: NexusRaven-V2 has never been trained on the functions used in evaluation.
🔥 Commercially Permissive: The training of NexusRaven-V2 does not involve any data generated by proprietary LLMs such as GPT-4. You have full control of the model when deployed in commercial applications.
NexusRaven-V2 accepts a list of python functions. These python functions can do anything (including sending GET/POST requests to external APIs!). The two requirements include the python function signature and the appropriate docstring to generate the function call.
NexusRaven-V2's Capabilities
NexusRaven-V2 is capable of generating deeply nested function calls, parallel function calls, and simple single calls. It can also justify the function calls it generated. If you would like to generate the call only, please set a stop criteria of "<bot_end>". Otherwise, please allow NexusRaven-V2 to run until its stop token (i.e. "</s>").
Quick Start Prompting Guide
Please refer to our notebook, How-To-Prompt.ipynb, for more advanced tutorials on using NexusRaven-V2!
We strongly recommend to set sampling to False when prompting NexusRaven-V2.
We strongly recommend a very low temperature (~0.001).
We strongly recommend following the prompting style below.
Quickstart
You can run the model on a GPU using the following code.
python
1# Please `pip install transformers accelerate`2from transformers import pipeline
345pipeline = pipeline(6"text-generation",7 model="Nexusflow/NexusRaven-V2-13B",8 torch_dtype="auto",9 device_map="auto",10)1112prompt_template = \
13'''
14Function:
15def get_weather_data(coordinates):
16 """
17 Fetches weather data from the Open-Meteo API for the given latitude and longitude.
1819 Args:
20 coordinates (tuple): The latitude of the location.
2122 Returns:
23 float: The current temperature in the coordinates you've asked for
24 """
2526Function:
27def get_coordinates_from_city(city_name):
28 """
29 Fetches the latitude and longitude of a given city name using the Maps.co Geocoding API.
3031 Args:
32 city_name (str): The name of the city.
3334 Returns:
35 tuple: The latitude and longitude of the city.
36 """
3738User Query: {query}<human_end>
3940'''4142prompt = prompt_template.format(query="What's the weather like in Seattle right now?")4344result = pipeline(prompt, max_new_tokens=2048, return_full_text=False, do_sample=False, temperature=0.001)[0]["generated_text"]45print(result)
This should generate the following:
Call: get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle'))<bot_end>
Thought: The function call `get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle'))` answers the question "What's the weather like in Seattle right now?" by following these steps:
1. `get_coordinates_from_city(city_name='Seattle')`: This function call fetches the latitude and longitude of the city "Seattle" using the Maps.co Geocoding API.
2. `get_weather_data(coordinates=...)`: This function call fetches the current weather data for the coordinates returned by the previous function call.
Therefore, the function call `get_weather_data(coordinates=get_coordinates_from_city(city_name='Seattle'))` answers the question "What's the weather like in Seattle right now?" by first fetching the coordinates of the city "Seattle" and then fetching the current weather data for those coordinates.
If you would like to prevent the generation of the explanation of the function call (for example, to save on inference tokens), please set a stopping criteria of <bot_end>.
Please follow this prompting template to maximize the performance of RavenV2.
For a deeper dive into the results, please see our Github README.
Limitations
The model works best when it is connected with a retriever when there are a multitude of functions, as a large number of functions will saturate the context window of this model.
The model can be prone to generate incorrect calls. Please ensure proper guardrails to capture errant behavior is in place.
The explanations generated by NexusRaven-V2 might be incorrect. Please ensure proper guardrails are present to capture errant behavior.
We thank the CodeLlama team for their amazing models!
@misc{rozière2023code,
title={Code Llama: Open Foundation Models for Code},
author={Baptiste Rozière and Jonas Gehring and Fabian Gloeckle and Sten Sootla and Itai Gat and Xiaoqing Ellen Tan and Yossi Adi and Jingyu Liu and Tal Remez and Jérémy Rapin and Artyom Kozhevnikov and Ivan Evtimov and Joanna Bitton and Manish Bhatt and Cristian Canton Ferrer and Aaron Grattafiori and Wenhan Xiong and Alexandre Défossez and Jade Copet and Faisal Azhar and Hugo Touvron and Louis Martin and Nicolas Usunier and Thomas Scialom and Gabriel Synnaeve},
year={2023},
eprint={2308.12950},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
Citation
@misc{nexusraven,
title={NexusRaven-V2: Surpassing GPT-4 for Zero-shot Function Calling},
author={Nexusflow.ai team},
year={2023},
url={https://nexusflow.ai/blogs/ravenv2}
}
Contact
Please join our Discord Channel to reach out for any issues and comments!
