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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Paper • 2203.05482 • Published • 6 -
Diverse Weight Averaging for Out-of-Distribution Generalization
Paper • 2205.09739 • Published • 1 -
Fusing finetuned models for better pretraining
Paper • 2204.03044 • Published • 6 -
Sudden Drops in the Loss: Syntax Acquisition, Phase Transitions, and Simplicity Bias in MLMs
Paper • 2309.07311 • Published • 4
Niels Horn
nilq
AI & ML interests
Natural language understanding, synthetic emotional speech, mechanistic interpretability.
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16
nilq/baby-python-mistral-1L-tiny-TinyStories-ft
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nilq/baby-python-mistral-1L-tiny-lua-ft
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nilq/baby-python-1L-mistral-lua-stories-slerp
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nilq/baby-python-mistral-1L-tiny-base
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nilq/lua-stories-slerp-mistral-1L-tiny
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nilq/lua-stories-slerp-mistral-2L-tiny
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nilq/mistral-2L-tiny
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nilq/lua-stories-linear-mistral-1L-tiny
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nilq/python-mistral-1L-mini
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nilq/mistral-1L-tiny
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datasets
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nilq/baby-python-and-tiny-stories-and-lua
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nilq/baby-python-and-lua
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nilq/baby-python-and-tiny-stories
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nilq/baby-python
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nilq/small-lua-stack
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nilq/small-python-stack
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nilq/babylm-100M
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nilq/babylm-10M
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