Generative augmentations for improved cardiac ultrasound segmentation using diffusion models - Nature
<a href="https://news.google.com/rss/articles/CBMiX0FVX3lxTFBjNXIwNmo3eTJQWjNXVzc5TkhqYnFDMjdSOFEtOW93NmFBaHBMTjdfSlAzeDIzR2g3OHVQYWpLS0t2YUhiVzNyX0k3YlV3RW9xeV9EbGYxUVRLZUpBcGUw?oc=5" target="_blank">Generative augmentations for improved cardiac ultrasound segmentation using diffusion models</a> <font color="#6f6f6f">Nature</font>
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I technically got an LLM running locally on a 1998 iMac G3 with 32 MB of RAM
Hardware: • Stock iMac G3 Rev B (October 1998). 233 MHz PowerPC 750, 32 MB RAM, Mac OS 8.5. No upgrades. • Model: Andrej Karpathy’s 260K TinyStories (Llama 2 architecture). ~1 MB checkpoint. Toolchain: • Cross-compiled from a Mac mini using Retro68 (GCC for classic Mac OS → PEF binaries) • Endian-swapped model + tokenizer from little-endian to big-endian for PowerPC • Files transferred via FTP to the iMac over Ethernet Challenges: • Mac OS 8.5 gives apps a tiny memory partition by default. Had to use MaxApplZone() + NewPtr() from the Mac Memory Manager to get enough heap • RetroConsole crashes on this hardware, so all output writes to a text file you open in SimpleText • The original llama2.c weight layout assumes n_kv_heads == n_heads. The 260K model uses grouped-query attention (kv_heads
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I technically got an LLM running locally on a 1998 iMac G3 with 32 MB of RAM
Hardware: • Stock iMac G3 Rev B (October 1998). 233 MHz PowerPC 750, 32 MB RAM, Mac OS 8.5. No upgrades. • Model: Andrej Karpathy’s 260K TinyStories (Llama 2 architecture). ~1 MB checkpoint. Toolchain: • Cross-compiled from a Mac mini using Retro68 (GCC for classic Mac OS → PEF binaries) • Endian-swapped model + tokenizer from little-endian to big-endian for PowerPC • Files transferred via FTP to the iMac over Ethernet Challenges: • Mac OS 8.5 gives apps a tiny memory partition by default. Had to use MaxApplZone() + NewPtr() from the Mac Memory Manager to get enough heap • RetroConsole crashes on this hardware, so all output writes to a text file you open in SimpleText • The original llama2.c weight layout assumes n_kv_heads == n_heads. The 260K model uses grouped-query attention (kv_heads
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