Adding v3

README.md

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

src/verysimplemoe/model.py

@@ -66,6 +66,17 @@ ARCH_PRESETS: dict[str, dict[str, int | float | str]] = {
         "router_noise_std": 0.1,
         "router_z_loss_coef": 1e-4,
     },
+    "v3-2l-32x1m": {
+        "arch": "v3-2l-32x1m",
+        "n_layers": 2,
+        "d_model": 500,
+        "n_heads": 10,
+        "n_experts": 32,
+        "active_experts": 4,
+        "expert_hidden_size": 1000,
+        "router_noise_std": 0.1,
+        "router_z_loss_coef": 1e-4,
+    },
 }