Llama-2-7b-chat-hf-function-calling
Maintainer: Trelis
47
👁️
| 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
The Llama-2-7b-chat-hf-function-calling model extends the popular Hugging Face Llama 2 models with function calling capabilities. Developed by Trelis, this model responds with a structured JSON argument containing the function name and arguments, allowing for seamless integration into applications that require programmatic interactions.
Model inputs and outputs
Inputs
- Text: The model takes text prompts as input, which can include instructions for the desired function to be executed.
Outputs
- Structured JSON: The model generates a JSON object with two key-value pairs - "function" (the name of the function) and "arguments" (the arguments for the function).
Capabilities
The Llama-2-7b-chat-hf-function-calling model is capable of understanding function call requests and generating the appropriate JSON response. This allows developers to easily incorporate the model's functionality into their applications, automating tasks and integrating with various systems.
What can I use it for?
With the function calling capabilities of this model, you can build applications that streamline workflows, automate repetitive tasks, and enhance user experiences. Some potential use cases include:
- Developing intelligent chatbots or virtual assistants that can execute specific functions on behalf of users
- Integrating the model into business software to enable natural language-driven automation
- Building productivity tools that allow users to issue commands and have the model handle the underlying logic
Things to try
One interesting aspect of this model is its ability to handle function calls with varying numbers of arguments, from 0 to 3. You can experiment with different function descriptions and prompts to see how the model responds, ensuring that the expected JSON format is generated correctly.
Additionally, you can explore how the model's performance scales with larger parameter sizes, such as the 13B, 70B, and other versions available from the Trelis creator profile.
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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