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Source channel @githubtrending · Post #15143 · Sep 14

#python#llms#mlx MLX LM is a Python tool that helps you run and fine-tune large language models (LLMs) efficiently on Apple Silicon Macs. It connects easily to thousands of models on Hugging Face, supports model quantization to save memory, and allows distributed training. You can generate text or chat with models via simple commands or Python code. It also offers features like prompt caching and memory optimization for handling long texts, making it faster and less resource-heavy. This means you can run powerful AI models locally on your Mac without needing expensive cloud services, saving cost and improving speed. https://github.com/ml-explore/mlx-lm

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Venture Village Wall 🦄

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

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

Venture Village Wall 🦄

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

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