As a funny aside, one of the early private beta stories I circulated among friends a few months ago as I was testing formats was literally titled “a model in your pocket” and about low-param OSS models. Whoops.
LLMS
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Compare GPT Models Simultaneously with This New Tool
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Ever wish you could compare GPT models simultaneously ? @zwack010 built a sample app so you can do it in seconds.
— Replit ⠕ (@Replit) 5 mai 2023
Just plug in your API key and start prompting. pic.twitter.com/fBCNbyR8PZEver wish you could compare GPT models simultaneously ? @zwack010 built a sample app so you can do it in seconds. Just plug in your API key and start prompting.
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GPT-4 Architecture: Parameter Count and Competitive Moat Analysis
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OpenAI is mentioned in here as having no moat, but OpenAI was also intentionally silent about the parameter count of GPT-4. I know some people have been flinging around a 1T parameter number for GPT-4 but it’s very possible that it isn’t a single 1T model and instead 1T total.
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Meta’s LLaMA Leak: Legal Implications and Open Source Adoption
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And you have to appreciate the stance here which is basically like “what is Meta going to do, sue all the people using the leaked LLaMA model?”
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Meta’s FAIR Outpaces Google in Open Source AI Leadership
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Meta comes out looking so good from this. Meta’s AI division FAIR has consistently outmaneuvered Google in the open source deep learning community, starting with PyTorch and now with LLaMA.
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Custom Language Models Spawn New Generation of AI Startups
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An entire generation of startups are going to be born and grow on the back of these technologies because they are so lightweight and cheap to make custom implementations. I have yet to talk to a company deploying language modeling that is not playing around with custom models.
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Open Source LLaMA Models Achieving Foundation Model Performance
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What is new is just how many times this has been applied to LLaMA and other models to squeeze near-equivalent foundation model performance out of a small number of parameters. But that’s basically what happens when the cat comes out of the bag in open source.
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GPT Quantization and LoRA: Historical Context of Optimization Techniques
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Those techniques are not explicitly new—GPT quantization goes back to October 2022 (and Google released a paper about leveraging sparsity in training in 2021). LoRA shows up in a paper in 2021.
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LoRA and Quantization: Google Shifts from Big Numbers Approach
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One of my big takeaways is just how important LoRA—basically a way to simplify and reduce the cost re-training models—and quantization for more compact models is here. Google has traditionally taken the Big Numbers approach to model development so it’s at minimum notable.
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Andrew Ling to Discuss LLM Architecture Choices at FCCM Panel
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Tune in on May 10 to hear Andrew Ling, PhD, Sr. Director, Software Engineering & ML Compiler participate in a FCCM panel about architecture choice for training and inference markets for Large Language Models (LLMs). Learn more at https://
fccm.org/panel-2023/