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390M_4M_2k.csv
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390M_700kbs_10k.csv
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390M_700kbs_10k/blumodel_muon_4M_step00008001.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:3405f8d0554ccf6e5f902442e6e90ee381e3c61497b5e0cf1fa8de33276ec5bb
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size 4205803653
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390M_700kbs_10k/blumodel_muon_4M_step00009000.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:28c48b00e453fbd2ccd627f003ab188e86d286946f3162e7163684c2f75e761b
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size 4205803653
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390M_700kbs_10k/blumodel_muon_4M_step00009967.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:34bfc300fcb6cdd1528d50480b9ddf755d1811513ab821c394bf5a198ae245b9
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size 4205803653
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390M_700kbs_9k.csv
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train_390M.py
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@@ -448,9 +448,9 @@ class ModelConfig:
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qk_eps: float = 1e-6 # QK-norm (fused) epsilon
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# ---- optimization / data ----
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B: int =
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T: int = 4096 # sequence length (<= context_length)
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global_batch: int =
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grad_clip: float = 1.0
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weight_decay: float = 0.1
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muon_lr: float = 0.001
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muon_min_lr: float = 0.0001
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max_steps: int =
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warmup_steps: int = 500
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stable_steps: int = int(max_steps * 0.99) - warmup_steps # steps held at max_lr after warmup
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qk_eps: float = 1e-6 # QK-norm (fused) epsilon
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# ---- optimization / data ----
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B: int = 4 # micro-batch
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T: int = 4096 # sequence length (<= context_length)
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global_batch: int = 786432 # tokens per optimizer step
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grad_clip: float = 1.0
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weight_decay: float = 0.1
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muon_lr: float = 0.001
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muon_min_lr: float = 0.0001
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max_steps: int = 17802
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warmup_steps: int = 500
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stable_steps: int = int(max_steps * 0.99) - warmup_steps # steps held at max_lr after warmup
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