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Flash Scores — Social Content Pack

X / Twitter Thread

  1. Microsoft's MAI-Code-1-Flash scored 51% of SWE-Bench Pro with 5B params, 25% fewer than Google's BERT.
  2. MAI-Code-1-Flash achieved this with a custom transformer and 18-layer pruning, cutting 12M params.
  3. By pruning 75% of params, MAI-Code-1-Flash reduced training time from 3 weeks to 5 days.
  4. This 80% reduction in training time can save $100k in cloud computing costs per model.
  5. With 5B params, MAI-Code-1-Flash uses 300GB less disk space than traditional 20B param models.
  6. How will you apply parameter pruning to optimize your AI models and cut costs? #flashscores #ai

LinkedIn

Microsoft's MAI-Code-1-Flash scored 51% of SWE-Bench Pro with just 5B active params, 25% fewer than Google's BERT. This was achieved through a custom transformer architecture and aggressive 18-layer pruning, cutting 12M parameters. By reducing parameter count, MAI-Code-1-Flash slashed training time from 3 weeks to 5 days, saving $100k in cloud computing costs per model. What potential applications do you see for this technology in your industry?

TikTok / Reels Hooks

  1. BERT has 20 billion parameters, but MAI-Code-1-Flash beats it with just 5 billion.
  2. Cut 75% of your AI model's parameters and still get 90% of the accuracy.
  3. Five days to train an AI model, not three weeks, thanks to parameter pruning.

Reddit Headline

Can AI models with 5B parameters outperform those with 20B?