xLAM-v0.1-r
Maintainer: Salesforce
47
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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
The xLAM-v0.1-r model is a large action model developed by Salesforce. It is an upgraded version of the Mixtral model, with significant improvements in many areas. The xLAM-v0.1-r model has been fine-tuned across a wide range of agent tasks and scenarios, while preserving the capabilities of the original Mixtral model. It is designed to enhance decision-making and translate user intentions into executable actions that interact with the world.
Model inputs and outputs
The xLAM-v0.1-r model is a text-to-text transformer model that can take in natural language prompts and generate corresponding responses.
Inputs
- Natural language prompts describing tasks or queries
Outputs
- Natural language responses that represent the model's interpretation and execution of the input prompt
Capabilities
The xLAM-v0.1-r model exhibits strong function-calling capabilities, allowing it to understand natural language instructions and execute corresponding API calls. This enables the model to interact with a variety of digital services and applications, such as retrieving weather information, managing social media platforms, and handling financial services.
What can I use it for?
The xLAM-v0.1-r model can be leveraged for a wide range of applications that require AI agents to autonomously plan and execute tasks to achieve specific goals. This includes workflow automation, personal assistant services, and task-oriented dialogue systems. The model's ability to translate natural language into structured API calls makes it well-suited for building intelligent software agents that can seamlessly integrate with various digital platforms and services.
Things to try
One interesting aspect of the xLAM-v0.1-r model is its ability to generate JSON-formatted responses that closely resemble the function-calling mode of ChatGPT. This can be particularly useful for building applications that require structured outputs for easy integration with other systems. Developers can experiment with different prompts and observe how the model translates natural language into executable function calls.
Another aspect to explore is the model's performance on the Berkeley Function-Calling Leaderboard (BFCL), where the xLAM-v0.1-r and its smaller counterpart xLAM-1b-fc-r have achieved competitive results. Investigating the model's strengths and weaknesses across the different categories on this benchmark can provide valuable insights for further improving function-calling capabilities.
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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