Nexus Raven is a 13B instruction tuned model for function calling tasks.
13b
39.6K Pulls Updated 11 months ago
Updated 11 months ago
11 months ago
2f770812d079 · 14GB
model
archllama
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parameters13B
·
quantizationQ8_0
14GB
template
{{ .System }}
User Query: {{ .Prompt }}<human_end>
52B
license
Nexusflow.ai License Terms
NexusRaven-V2 Version Release Date: December 5, 2023
“Agreement” m
6.7kB
Readme
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
Function:
def get_weather_data(coordinates):
"""
Fetches weather data from the Open-Meteo API for the given latitude and longitude.
Args:
coordinates (tuple): The latitude of the location.
Returns:
float: The current temperature in the coordinates you've asked for
"""
Function:
def get_coordinates_from_city(city_name):
"""
Fetches the latitude and longitude of a given city name using the Maps.co Geocoding API.
Args:
city_name (str): The name of the city.
Returns:
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 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.