Edge 436: Salesforce's xLAM is a New Model for Agentic Tasks
The new model excels in tasls such as function calling, tool integration and planning.
Agentic workflows is one of the most interesting categories in foundation model research. By agentic workflows we are referring to AI programs that can execute actions in a specific environment. One of the main debates in the agent community is how many capabilities go into a model versus peripherical methods like RAG. Recently, Salesforce Research published some major work with agentic AI with xLAM, a series of models optimized for agentic tasks.
xLAM is a new series of action models designed specifically for AI tasks. It includes five different models, built using either dense or mixture-of-expert architectures. These models range in size from 1 billion to 8x22 billion parameters. A flexible and scalable training pipeline was used to enhance their performance across a variety of environments by combining and augmenting diverse datasets. Initial tests show that xLAM consistently performs well, placing first on the Berkeley Function-Calling Leaderboard and surpassing other prominent models like GPT-4 and Claude-3 in specific tasks, particularly those requiring tool use.