In Honour of This Year's NeurIPs Test of Time Paper Awardees
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2
Jaward Sesay
Jaward
AI & ML interests
I like to train large deep neural nets too 🧠🤖💥 | First Paper (AutoAgents: A Framework for Automatic Agent Generation) Accepted @ IJCAI 2024 | Role Model Karpathy
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Qwen/QVQ-72B-Preview
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nanoBLT: Simplified lightweight implementation of a character-level Byte Latent Transformer model (under 500 lines of code). The model is 2x4x2 (n_layers_encoder, n_layers_latent, n_layers_decoder) layer deep trained on ~1M bytes of tiny Shakespeare with a patch size of 4.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/byte_latent_transformer.ipynb
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deepseek-ai/DeepSeek-V3-Base
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nanoBLT: Simplified lightweight implementation of a character-level Byte Latent Transformer model (under 500 lines of code). The model is 2x4x2 (n_layers_encoder, n_layers_latent, n_layers_decoder) layer deep trained on ~1M bytes of tiny Shakespeare with a patch size of 4.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/byte_latent_transformer.ipynb
Code: https://github.com/Jaykef/ai-algorithms/blob/main/byte_latent_transformer.ipynb
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Implements from first-principle a discrete flow matching model for code generation- trained a small sized 2D dfm model on two variations of code for binary search. The result was amazing, code in comment:
Code: https://github.com/Jaykef/ai-algorithms/blob/main/dfm.ipynb
Code: https://github.com/Jaykef/ai-algorithms/blob/main/dfm.ipynb
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