content.raw: Just released NVAMP Loss!
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content.value: Just released NVAMP Loss!
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content.raw: ✔️ modification of the cross-entropy loss function designed specifically for training LLMs. 
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content.value: ✔️ modification of the cross-entropy loss function designed specifically for training LLMs. 
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content.raw: ✔️ twist on the standard cross-entropy loss by emphasizing the importance of outlier prediction errors and dynamically normalizing token-level variance. 
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content.value: ✔️ twist on the standard cross-entropy loss by emphasizing the importance of outlier prediction errors and dynamically normalizing token-level variance. 
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content.raw: ✔️ more stable and efficient training, leading to models that generalize better. 
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content.value: ✔️ more stable and efficient training, leading to models that generalize better. 
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content.raw: Check it out, give it a spin, and let me know what you think!
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content.value: Check it out, give it a spin, and let me know what you think!
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content.raw: Licensed under the Apache 2.0 license and ready to use. Happy training! 🔥🤖
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content.value: Licensed under the Apache 2.0 license and ready to use. Happy training! 🔥🤖
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content.href: https://github.com/mkurman/nvamp-loss
content.raw: https://github.com/mkurman/nvamp-loss
content.type: link
rawContent: Just released NVAMP Loss!
  
  ✔️ modification of the cross-entropy loss function designed specifically for training LLMs. 
  ✔️ twist on the standard cross-entropy loss by emphasizing the importance of outlier prediction errors and dynamically normalizing token-level variance. 
  ✔️ more stable and efficient training, leading to models that generalize better. 
  
  Check it out, give it a spin, and let me know what you think!
  
  Licensed under the Apache 2.0 license and ready to use. Happy training! 🔥🤖
  
  https://github.com/mkurman/nvamp-loss
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