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Source channel @githubtrending · Post #15141 · Sep 13

#python#large_language_models#machine_learning_systems#natural_language_processing Flash Linear Attention (FLA) is a fast, memory-efficient library for advanced linear attention models used in transformers, written in PyTorch and Triton, and compatible with NVIDIA, AMD, and Intel GPUs. It offers many state-of-the-art linear attention models and fused modules that speed up training and reduce memory use. You can easily replace standard attention layers in your models with FLA’s efficient versions, improving training and inference speed, especially for long sequences. FLA supports hybrid models mixing linear and standard attention, and integrates with Hugging Face Transformers for easy use and evaluation. This helps you train and run large language models faster and with less memory, making your AI projects more efficient and scalable. https://github.com/fla-org/flash-linear-attention

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Venture Village Wall 🦄

@venturevillagewall · Post #3607 · 12/20/2024, 07:00 PM

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o3 & o3-mini Break Benchmark Records The performance of o3 and o3-mini showcases state-of-the-art (SOTA) results across various benchmarks. Key insights include: - Frontier Math scores increased from 2% to 25%. - SWE-Bench achieved 71.7%, a significant leap for a startup that recently raised $200 million with 13.86% earlier this year. - ELO on Codeforces reached 2727, held by only 150 individuals globally. - ARC-AGI model scored 87.5%, breaking a five-year deadlock. - Noteworthy progress on GPQA and AIME benchmarks. Access to o3-mini is currently available to security researchers, while general public access is set for late January. Full access to o3 will follow later. #AI#SOTA#Benchmarks#o3#o3-mini #FrontierMath#SWE-Bench #Codeforces#ELO#ARC-AGI #GPQA#AIME#Funding#Progress#Research#Technology#Innovation

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Venture Village Wall 🦄

@venturevillagewall · Post #3606 · 12/20/2024, 06:41 PM

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O3 and O3-Mini Benchmark Breakthroughs The O3 and O3-Mini models showcase state-of-the-art (SOTA) performance with significant leaps in various benchmarks. Results on Frontier Math have jumped from 2% to 25%. The SWE-Bench model achieved a score of 71.7%, while a startup has raised $200 million following results of 13.86%. ELO on Codeforces reached 2727, surpassing most peers globally. Notably, the ARC-AGI model scored 87.5%, breaking a five-year benchmark. Access for security researchers to O3-Mini starts today, with general access available in late January. #O3#O3Mini#SOTA#Benchmarks#AI#ML#Funding#Codeforces#ARC-AGI #FrontierMath#SWE-Bench #ELO#GPQA#AIME#SecurityResearch#TechUpdates#Innovations#Startups#Performance#AIModels

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