NexusRaven-13B
Maintainer: Nexusflow
97
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
NexusRaven-13B is an open-source and commercially viable function calling language model developed by Nexusflow that surpasses the state-of-the-art in function calling capabilities. It was fine-tuned from the codellama/CodeLlama-13b-Instruct-hf model. Compared to GPT-4, NexusRaven-13B achieves a 95% success rate in using cybersecurity tools like CVE/CPE Search and VirusTotal, while GPT-4 achieves 64%. It has significantly lower cost and faster inference speed. NexusRaven-13B also generalizes well to tools never seen during training, achieving performance comparable to GPT-3.5 in zero-shot settings, outperforming other open-source LLMs of similar sizes.
Model inputs and outputs
NexusRaven-13B is a function calling language model that takes in a list of Python functions with their docstrings and generates JSON outputs with the function name and arguments. The model works best when provided with well-documented functions that have arguments, whether required or optional.
Inputs
- Functions: A list of Python functions with their docstrings
- User Query: A prompt for the model to generate a function call response to
Outputs
- Function Call: A JSON object with the function name and argument values
- Explanation (optional): A detailed explanation of the generated function call
Capabilities
NexusRaven-13B is capable of generating single function calls, nested calls, and parallel calls in many challenging cases. It can also provide detailed explanations for the function calls it generates, which can be turned off to save tokens during inference.
What can I use it for?
NexusRaven-13B can be used in a variety of applications that require interacting with APIs or executing functions based on user prompts. For example, you could use it to build a chatbot that can perform web scraping, make API calls, or execute other programmatic tasks on demand. The model's strong performance on cybersecurity tools makes it a promising candidate for building security-focused applications.
Things to try
One interesting thing to try with NexusRaven-13B is to provide it with a set of functions that interact with external APIs, such as fetching weather data or geolocating a city. You can then prompt the model to generate function calls that combine these capabilities to answer complex user queries, like "What's the weather like in Seattle right now?". The model's ability to chain together function calls and provide detailed explanations can make it a powerful tool for building conversational AI applications.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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