Nexus Raven is a 13B instruction tuned model for function calling tasks.


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license License Terms NexusRaven-V2 Version Release Date: December 5, 2023 “Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Nexusflow Materials set forth herein. “Documentation” means the specifications, manuals and documentation accompanying NeuxsRaven-V2 distributed by Nexusflow at, if any. “Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf. “NexusRaven-V2” means the large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing made available by Nexusflow at “Nexusflow Materials” means, collectively, Nexusflow’s proprietary NexusRaven-V2 and Documentation (and any portion thereof) made available under this Agreement. “Nexusflow” or “we” means Inc. 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{{ .System }} User Query: {{ .Prompt }}<human_end>


Nexus Raven is a 13 billion parameter model designed for function calling tasks.

Updated to NexusRaven-V2, this model is an updated open-source and commercially viable function calling model:

  • Versatile Function Calling Capability: capable of generating single function calls, nested calls, and parallel calls in many challenging cases.
  • Fully Explainable: 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: surpasses GPT-4 by up to 7% in function calling success rates in human-generated use cases involving nested and composite functions.
  • Generalization to the Unseen: has never been trained on the functions used in evaluation.
  • Commercially Permissive: The training 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.

Example prompt

def get_weather_data(coordinates):
    Fetches weather data from the Open-Meteo API for the given latitude and longitude.

    coordinates (tuple): The latitude of the location.

    float: The current temperature in the coordinates you've asked for

def get_coordinates_from_city(city_name):
    Fetches the latitude and longitude of a given city name using the Geocoding API.

    city_name (str): The name of the city.

    tuple: The latitude and longitude of the city.

User Query: {query}<human_end>

This will generate a response similar to:

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 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.



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