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Oner Yalcin
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oneryalcin
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I made a 1.1M ModernBERT encoder play Doom in real time on a CPU Some time ago, VAGO Solutions released SauerkrautLM-Doom-MultiVec-1.3M, a tiny model trained to play Doom Defend the Center scenario from 31k human gameplay examples. My first thought: cool! I love both Doom and Small Language Models. Then another idea: I bet I can do better :-) What I did? - evaluated the original model and found it's better than reported - changed a bit the architecture - generated SFT data with a scripted oracle - SFT + PPO refinement on consumer hardware Got a smaller, faster and killer model Can even fit a floppy with int8 quantization 💾 Watch it play/read the article: https://huggingface.co/spaces/anakin87/tiny-doom-defender
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New activity in
opensearch-project/opensearch-neural-sparse-encoding-v2-distill
almost 2 years ago
Code for training / finetuning the sparse encoders
13
#2 opened about 2 years ago by
oneryalcin
New activity in
opensearch-project/opensearch-neural-sparse-encoding-v2-distill
about 2 years ago
Code for training / finetuning the sparse encoders
13
#2 opened about 2 years ago by
oneryalcin
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