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Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimization

Created by
  • Haebom

Author

Christopher Subia-Waud (Rayonlabs Team)

Outline

This paper points out the insufficient performance of existing AutoML platforms and proposes Gradients, a distributed system. Based on the Bittensor network, Gradients is a competitive system where independent miners compete to find optimal hyperparameters and receive rewards proportional to their performance. Experimental results show that Gradients achieved a 100% win rate compared to TogetherAI, Databricks, and Google Cloud, and an 82.8% win rate compared to HuggingFace AutoTrain. It achieved an average performance improvement of 42.1% compared to commercial platforms, with 30-40% and 23.4% performance gains for retrieval-augmented generation and diffusion models, respectively. This demonstrates that a distributed system with economic incentives can outperform existing centralized AutoML.

Takeaways, Limitations

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Takeaways:
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We demonstrate that decentralized systems and economic incentives are effective in improving AutoML performance.
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Presenting a new approach that overcomes the limitations of existing AutoML platforms.
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We have confirmed the possibility of significant performance improvements in specific tasks such as retrieval-augmented generation and diffusion models.
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Presenting the possibility of AutoML optimization using market competition mechanisms.
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Limitations:
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The performance improvements of gradients may be limited to specific datasets and tasks.
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Further verification of the stability and scalability of the Bittensor network is needed.
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Analysis of the implementation and operational costs of Gradients is needed.
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Further research is needed on generalizability across different model sizes and tasks.
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