Salesforce proves less is more: xLAM-1B ‘Tiny Giant’ beats bigger AI Models

by | Jul 3, 2024 | Technology

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Salesforce has unveiled an AI model that punches well above its weight class, potentially reshaping the landscape of on-device artificial intelligence. The company’s new xLAM-1B model, dubbed the “Tiny Giant,” boasts just 1 billion parameters yet outperforms much larger models in function-calling tasks, including those from industry leaders OpenAI and Anthropic.

Meet Salesforce Einstein “Tiny Giant.” Our 1B parameter model xLAM-1B is now the best micro model for function calling, outperforming models 7x its size, including GPT-3.5 & Claude. On-device agentic AI is here. Congrats Salesforce Research!Paper: https://t.co/SrntYvgxR5… pic.twitter.com/pPgIzk82xT— Marc Benioff (@Benioff) July 3, 2024

This David-versus-Goliath scenario in the AI world stems from Salesforce AI Research‘s innovative approach to data curation. The team developed APIGen, an automated pipeline that generates high-quality, diverse, and verifiable datasets for training AI models in function-calling applications.

“We demonstrate that models trained with our curated datasets, even with only 7B parameters, can achieve state-of-the-art performance on the Berkeley Function-Calling Benchmark, outperforming multiple GPT-4 models,” the researchers write in their paper. “Moreover, our 1B model achieves exceptional performance, surpassing GPT-3.5-Turbo and Claude-3 Haiku.”

Small but mighty: The power of efficient AI

This achievement is particularly noteworthy given the model’s compact size, which makes it suitable for on-device applications where larger models would be impractical. The implications for enterprise AI are significant, potentially allowing for more powerful and responsive AI assistants that can run locally on smartphones or other devices with limited computing resources.

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The key to xLAM-1B’s performance lies in the quality and diversity of its training data. The APIGen pipeline leverages 3,673 executable APIs across 2 …

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Don’t miss OpenAI, Chevron, Nvidia, Kaiser Permanente, and Capital One leaders only at VentureBeat Transform 2024. Gain essential insights about GenAI and expand your network at this exclusive three day event. Learn More

Salesforce has unveiled an AI model that punches well above its weight class, potentially reshaping the landscape of on-device artificial intelligence. The company’s new xLAM-1B model, dubbed the “Tiny Giant,” boasts just 1 billion parameters yet outperforms much larger models in function-calling tasks, including those from industry leaders OpenAI and Anthropic.

Meet Salesforce Einstein “Tiny Giant.” Our 1B parameter model xLAM-1B is now the best micro model for function calling, outperforming models 7x its size, including GPT-3.5 & Claude. On-device agentic AI is here. Congrats Salesforce Research!Paper: https://t.co/SrntYvgxR5… pic.twitter.com/pPgIzk82xT— Marc Benioff (@Benioff) July 3, 2024

This David-versus-Goliath scenario in the AI world stems from Salesforce AI Research‘s innovative approach to data curation. The team developed APIGen, an automated pipeline that generates high-quality, diverse, and verifiable datasets for training AI models in function-calling applications.

“We demonstrate that models trained with our curated datasets, even with only 7B parameters, can achieve state-of-the-art performance on the Berkeley Function-Calling Benchmark, outperforming multiple GPT-4 models,” the researchers write in their paper. “Moreover, our 1B model achieves exceptional performance, surpassing GPT-3.5-Turbo and Claude-3 Haiku.”

Small but mighty: The power of efficient AI

This achievement is particularly noteworthy given the model’s compact size, which makes it suitable for on-device applications where larger models would be impractical. The implications for enterprise AI are significant, potentially allowing for more powerful and responsive AI assistants that can run locally on smartphones or other devices with limited computing resources.

Countdown to VB Transform 2024

Join enterprise leaders in San Francisco from July 9 to 11 for our flagship AI event. Connect with peers, explore the opportunities and challenges of Generative AI, and learn how to integrate AI applications into your industry. Register Now

The key to xLAM-1B’s performance lies in the quality and diversity of its training data. The APIGen pipeline leverages 3,673 executable APIs across 2 …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]

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