xLAM
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Xlam
Overview :
xLAM is an intelligent agent research project based on Large Language Models (LLMs) developed by the Salesforce AI Research team. It aggregates intelligent agent trajectories from various environments, standardizing and unifying them into a consistent format to create an optimized general data loader specifically designed for intelligent agent training. xLAM-v0.1-r is the 0.1 version of this model series, designed for research purposes and compatible with VLLM and FastChat platforms.
Target Users :
xLAM is primarily targeted towards researchers and developers in the field of artificial intelligence, particularly those specializing in large language models and intelligent agent technologies. It provides a powerful tool to help them more efficiently train and research intelligent agents.
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Use Cases
Researchers use the xLAM model to train intelligent agents in multi-turn dialogue scenarios
Developers utilize xLAM to develop and optimize intelligent customer service systems
Educational institutions use xLAM as a teaching tool to instruct students on how to use and develop LLMs-based intelligent systems
Features
Support aggregation and unification of intelligent agent trajectories from multiple environments
An optimized general data loader specifically designed for intelligent agent training
Maintain balance between different data sources and ensure independence randomization between devices
Compatibility with VLLM and FastChat platforms
Model fine-tuning to adapt to a wide range of intelligent agent tasks and scenarios
Provide detailed installation and training guidelines to enable researchers to quickly get started
How to Use
1. Access the xLAM GitHub page and clone or download the project code
2. Configure the docker environment according to the provided installation guide or install dependencies using pip
3. Read the documentation to understand the model's architecture and functionality
4. Start training intelligent agents using the provided data loader and training scripts
5. Adjust model parameters and training configurations as needed to optimize performance
6. Run benchmark tests to evaluate the performance of the intelligent agents
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