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🔄 In a Training Loop

Ash W.

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AI & ML interests

Datasets and parameter efficiency. contact at: ash@basicallyai.co

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reacted to onekq's post with 🔥 1 day ago
Lots of attentions are now on GPU residual value. I find car analogy to be useful. * Both new and used cars can do the same job (you can run the latest model on 6-year-old A100) * New cars are more efficient (higher performance per power draw) * It's not the year, but mileage and maintenance The last point is undeveloped. We need Carfax and KBB for GPUs. And there will be lemon GPUs.
repliedto Banaxi-Tech's post 1 day ago
We're excited to open source BananaMind OS, our OS for running BananaMind models. It allows you to run local AI on hardware such as a 486 or 386 (untested) Check it out at: https://github.com/BananaMind/BananaMindOS How to use it, either build it yourself (see instructions on repo) or check out our prebuilt ISOs: https://github.com/BananaMind/BananaMindOS/releases/download/v0.0.2/bananamind-os.iso is for 6MB+ RAM using Grub https://github.com/BananaMind/BananaMindOS/releases/download/v0.0.2/bananamind-ultra.iso is for under 6MB RAM using our custom bootloader to bypass the grub memory requirement. The ISOs are about 70MB because they include all of the models in our custom format, see repo if you want to build a ISO for only one model. x64 and x86 supported. 16 bit maybe soon. And a TTS on 486 soon.
reacted to onekq's post with ❤️ 4 days ago
This is an easy-to-remember pattern. GLM and Kimi are in Beijing, DeepSeek and Qwen are in Hangzhou. OpenAI and Anthropic are in SF, xAI and Meta are in the peninsula. In both China and the Bay Area, token price of one area is ~1/3 of the other area.
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