Adding v3
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@@ -26,6 +26,12 @@ The new research architecture is available as `v2-32x1m`:
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- router z-loss to keep router logits stable
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- optional phased expert training so only a subset of experts are trainable/routeable at a time
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The early v3 preset is `v3-2l-32x1m`:
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- same 32 experts/layer and 1M params/expert as v2
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- 2 transformer/MoE layers instead of 1
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- intended as the first quality-focused depth increase while keeping the v2 expert shape intact
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The default still uses `n_layers=1`, which means the model has one MoE block. If you raise `--n-layers`, each layer gets its own full expert set.
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## Install
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@@ -67,6 +73,25 @@ verysimplemoe-train \
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--block-size 256
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```
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## Train early v3: 2 layers, 32 experts/layer, 1M params/expert
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Recommended GPU command for a 50M-token FineWeb EDU run:
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```bash
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verysimplemoe-train \
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--arch v3-2l-32x1m \
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--dataset-name HuggingFaceFW/fineweb-edu \
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--dataset-config sample-10BT \
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--out-dir checkpoints/verysimplemoe-v3-2l-32x1m-fineweb-edu-50m \
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--max-steps 6104 \
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--batch-size 8 \
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--grad-accum-steps 4 \
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--block-size 256 \
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--train-experts-per-phase 16 \
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--expert-phase-steps 500 \
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--save-every 1000
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```
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Phased expert training means:
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- only 16 of the 32 experts are eligible for routing in a given phase
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