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Donkey Small

DonkeySmall
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reacted to schuler's post with ❤️ 3 days ago
🔮 GPT-3 implemented in pure Free Pascal! https://github.com/joaopauloschuler/gpt-3-for-pascal This implementation follows the GPT-3 Small architecture from the landmark paper "Language Models are Few-Shot Learners": ``` ┌─────────────────────────┐ │ Input Layer │ ├─────────────────────────┤ │ Token & Positional │ │ Embedding │ ├─────────────────────────┤ │ 12x Transformer │ │ Blocks │ │ - 12 heads │ │ - 768 hidden dims │ │ - 3072 intermediate │ ├─────────────────────────┤ │ Output Layer │ └─────────────────────────┘ ``` Clean Pascal Implementation ``` for CntLayer := 1 to {Layers=}12 do begin Result.AddTransformerBlockCAI( {Heads=}12, {intermediate dimensions=}4*768, {NoForward=}true, {HasNorm=}true, false ); end; ```
liked a Space 3 months ago
ZhengPeng7/BiRefNet_demo
reacted to lorraine2's post with 👍 3 months ago
New NVIDIA paper: ⚡ Multi-student Diffusion Distillation for Better One-step Generators ⚡ Do you want to make your diffusion models (a) run in a single step, (b) run with a smaller model, and (c) have improved quality simultaneously? Check out our multi-student distillation (MSD) method, which is simple and applicable to most diffusion models! The only catch is now we have to distill (and store) a mixture-of-expert student generators. Explore the MSD project page to learn more: https://research.nvidia.com/labs/toronto-ai/MSD/ Work led by Yanke Song along with Weili Nie, Karsten Kreis and James Lucas Check out more work from the Toronto AI Lab here: https://research.nvidia.com/labs/toronto-ai/
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