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30 prompts fine-tune AI for optimal energy storage control

By PulseAugur Editorial · [1 sources] ·

Researchers have demonstrated that using just 30 specific prompts can significantly optimize an open-weight AI model for energy storage control. This fine-tuning approach reduced the model's building emissions to 61.2 kg-CO2, closely approaching the optimal target of 60.8 kg-CO2, according to a preprint study. AI

IMPACT Demonstrates a low-cost method for optimizing AI models for energy efficiency in control systems.

RANK_REASON The cluster describes a research finding on fine-tuning an AI model for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

30 prompts fine-tune AI for optimal energy storage control

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The cluster describes a research finding on fine-tuning an AI model for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    30 prompts fine-tune AI to near-optimal energy storage control Reinforcement fine-tuning with 30 prompts cut an open-weight model's building emissions to 61.2 k

    30 prompts fine-tune AI to near-optimal energy storage control Reinforcement fine-tuning with 30 prompts cut an open-weight model's building emissions to 61.2 kg-CO2, near the 60.8 optimum, a preprint shows. https://www. notatechguy.com/30-prompts-fin e-tune-ai-to-near-optimal-en…

    LINKS notatechguy.com/30-prompts-fine-tune-ai-t…

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