Quantization in modern LLMs - Advanced Quantization Techniques for Large Language Models Video Tutorial | LinkedIn Learning, formerly Lynda.com
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Local LLMs on Consumer Hardware: GLM-4.7-Flash Performance | Hammad Armghan, PhD posted on the topic | LinkedIn
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The magic behind LLMs from tokenization all the way to final prediction in under 1 min ✨ I’m obsessed with digging into every detail of LLMs and I found this visualization incredibly helpful for… | Amer H. | 11 comments
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MLX MiniMax 2.5 running LOCALLY on a single M3 Ultra 512GB! Writing a poem on LLMs at 6bit quantization! 🔥 Let's start some coding, context and distributed tests! Generation: 40.2 tokens-per-sec Peak memory: 186 GB Source: Ivan Fioravanti | Thanh Hoang
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LLMs can take gigabytes of memory to store, which limits what can be run on consumer hardware. But quantization can dramatically compress models, making a wider selection of models available to developers. You can often reduce model size by 4x or more while maintaining reasonable performance. In our new short course Quantization Fundamentals taught by Hugging Face's Younes Belkada and Marc Sun, you'll: - Learn how to quantize nearly any open source model - Use int8 and bfloat16 (Brain float 16)
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