Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey PEFT algorithms are useful for dealing with LLMs with high parameter counts, as even fine-tuning these models from scratch can be computationally expensive and resource-intensive. https://
arxiv.org/abs/2403.14608
@hardmaru
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Parameter-Efficient Fine-Tuning Methods for Large Language Models
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Sometimes Avoiding Auto-Differentiable Methods in Development
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true-sometimes I try not to use auto-differentiable methods 🙂
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Japan’s Growing Appeal to Venture Capital and Risk-Taking Entrepreneurs
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Japan’s New Appetite for Risk Lures Venture Investors to Tokyo “If we had started @SakanaAILabs in the Bay Area, it would have been a strategic blunder, because we would have looked more like everyone else. It would have been very hard to differentiate.”
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Meta does what OpenAI should be doing – Discussion
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Meta does everything OpenAI should be Discussion on r/MachineLearning https://redd.it/1cbhec7
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Sakana AI Labs Hiring HR and Accounting Manager
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We are looking to hire an HR & Accounting manager to help @SakanaAILabs expand, as we grow our team! If you are interested, or know someone who might be, please check out our careers website for more information: https://
sakana.ai/careers -
Apache-2.0 Licenses Needed for Open-Source AI Acceleration
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Although I must say that we need more Apache-2.0 style licenses, and less of these “non-commercial, research only” restrictive ‘community’ licenses for model releases to really accelerate open-source AI adoption.
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Implementation Discovery of Speed Enhancement Through EvoSDXL-JP Model Merging
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Wow, this is amazing! I tried to download a model from HuggingFace and run it, but CUDA ran out of memory. That's odd, so I looked at the contents of the .py file and found that it appears to be implemented as "Download 4 ingredient models and merge synthesize EvoSDXL-JP on your end"…! So you can actually achieve speed enhancement just by merging…! x.com/iwiwi/status/1…
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Open-source AI models: commitment and strategic choices
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I have some opinions on this matter. I don’t think everything needs to be open-source, but for models that companies decide that it is strategically better for them to release, they might as well go all the way.
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Apache-2 Licensing vs Restrictive Community Licenses Debate
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This is controversial, but I’d rather see companies release Apache-2 licensed models, but keep certain models proprietary, than release everything under some non-open-source restrictive “community” license, which has the adverse effect of encouraging and normalizing restrictions.
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Need Apache-2.0 Licenses for Open-Source AI Acceleration
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Although I must say that we need more Apache-2.0 style licenses, and less of these “non-commercial, research only” restrictive ‘community’ licenses for model releases to really accelerate open-source AI adoption.