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WW-PGD: Projected Gradient Descent optimizer

Announcing: ๐—ช๐—ช-๐—ฃ๐—š๐—— โ€” ๐—ช๐—ฒ๐—ถ๐—ด๐—ต๐˜๐—ช๐—ฎ๐˜๐—ฐ๐—ต๐—ฒ๐—ฟ ๐—ฃ๐—ฟ๐—ผ๐—ท๐—ฒ๐—ฐ๐˜๐—ฒ๐—ฑ ๐—š๐—ฟ๐—ฎ๐—ฑ๐—ถ๐—ฒ๐—ป๐˜ ๐——๐—ฒ๐˜€๐—ฐ๐—ฒ๐—ป๐˜ ๐Ÿš€ I just released WW-PGD, a small PyTorch add-on that wraps standard optimizers More

WeightWatcher, HTSR theory, and the Renormalization Group

There is a deep connection between the open-source weightwatcher tool, which implements ideas from the theory of Heavy Tailed Self-Regularization More

Fine-Tuned Llama3.2: Bad Instructions ?

Recently, Meta released LLama3.2 1B and 3B Instruct Fine Tuned LLM. To mixed reviews. On the one hand, it s ranking More

What’s instructive about Instruct Fine-Tuning: a weightwatcher analysis

Are you Fine-Tuning an open-source LLMs ? Like Llama, Mistral, or Qwen? A That is, Instruct Fine Tuning. Whether you More

Describing Double Descent with WeightWatcher

Double Descent (DD) is something that has surprised statisticians, computer scientists, and deep learning practitioners but it was known in the More

SVDSmoothing LLM Layers with WeightWatcher

Recently, Microsoft Research published the LASER method: Layer-Selective Rank Reduction in this recent, very popular paper The Truth is in There: More

Evaluating LLMs with WeightWatcher Part III: The Magic of Mistral, a Story of Dragon Kings

Recently, the Mistral models have taken the LLM world by storm. The Mistral Mixture of Experts (MOE) 8x7b model outperforms other More

Evaluating Fine-Tuned LLMs with WeightWatcher Part II: PEFT / LoRa Models

Evaluating LLMs is hard. Especially when you don t have a lot of test data.In the last post, we saw how to More

Evaluating Fine-Tuned LLMs with WeightWatcher

if you are fine-tuning your own LLMs, you need a way to evaluate them. And while there are over a dozen More

WeightWatcher new feature: fix_fingers=’clip_xmax’

WeightWatcher 0.7 has just been released, and it includes the new and improved advanced feature for analyzing Deep Neural Networks More