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Lee Park

gogo8232

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upvoted an article 27 days ago

SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory

upvoted a paper about 1 month ago

HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems

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gogo8232's activity

upvoted an article 27 days ago

Article

SAMURAI: Adapting Segment Anything Model for Zero-Shot Visual Tracking with Motion-Aware Memory

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Nov 23

• 7

upvoted a paper about 1 month ago

HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems

Paper • 2411.02959 • Published Nov 5 • 64

upvoted an article 3 months ago

Article

From DeepSpeed to FSDP and Back Again with Hugging Face Accelerate

Jun 13

• 45

updated 2 datasets 5 months ago

gogo8232/ucla-agi-data-mistral-7b-instruct-sppo-iter1_perplexities

Viewer • Updated Jul 18 • 19.8k • 34

gogo8232/ucla-agi-data-mistral-7b-instruct-sppo-iter1

Viewer • Updated Jul 14 • 19.8k • 33

updated 4 datasets 6 months ago

upvoted an article 6 months ago

Article

Our Transformers Code Agent beats the GAIA benchmark!

Jul 1

• 48

reacted to yushun0410's post with 🔥 6 months ago

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Hi Huggingfacers!

Thrilled to introduce Adam-mini, an optimizer that achieves on-par or better performance than AdamW with 45% to 50% less memory footprint. Adam-mini can also achieve 49.5% higher throughput than AdamW on Llama2-7B pre-training.

The design of Adam-mini is inspired by certain Hessian structures we observed on Transformers.

Feel free to try it out! Try switching to Adam-mini with the same hyperparams of AdamW, it would work with only half memory. Hope Adam-mini can help save time, cost, and energy in your tasks!

Paper: "Adam-mini: Use Fewer Learning Rates To Gain More" https://arxiv.org/abs/2406.16793

Code: https://github.com/zyushun/Adam-mini