training: use rank 64, define as single constant

- DEFAULT_RANK = 64 in train_router.py
- All references use the constant, not magic numbers
- ~2.5GB optimizer state instead of ~10GB

Co-Authored-By: Proof of Concept <poc@bcachefs.org>

training/DESIGN.md

@@ -3,7 +3,7 @@
 ## Overview
 
 Continuous fine-tuning of Qwen3.5-27B alongside live vLLM inference.
-Full-weight updates (not LoRA) using Apollo optimizer with rank-256
+Full-weight updates (not LoRA) using Apollo optimizer with rank-64
 gradient projection. No pause required — HOGWILD concurrent training.
 Weights shared via CUDA IPC between vLLM and the training process.
 
@@ -63,10 +63,9 @@ LoRA trains adapter matrices, not base weights. For personality and
 behavioral changes that persist as disposition, the base weights
 need to change. Apollo makes this memory-feasible.
 
-### Rank 256
-Not Mini (rank-1). With 100+ diverse training examples, the
-gradient's effective dimensionality can reach hundreds. Rank-256
-captures the structure. Memory cost: ~10GB (negligible on B200).
+### Rank 64
+Not Mini (rank-1). Rank-64 captures gradient structure across diverse
+training examples while keeping memory low (~2.5GB on 27B model).
 Compute cost: <0.25% of forward+backward.
 
 ### Channel-wise scaling
@@ -94,7 +93,7 @@ from a per-parameter seed each step.
 ### Parameter grouping (Qwen3.5 gotcha)
 conv1d weights are 3D tensors [10240, 1, 4]. Apollo's projector
 needs 2D matrices with min dimension >= rank. Small/3D tensors
-use standard Adam. Large 2D matrices use Apollo with rank-256.
+use standard Adam. Large 2D matrices use Apollo.
 
 ## Training Data Pipeline
 
@@ -229,7 +228,7 @@ a few hundred MB.
 
 | State | Location | Notes |
 |-------|----------|-------|
-| Apollo optimizer | train_router._optimizer | ~10GB for rank-256. Persisted to `/tmp/apollo_optimizer_state.pt` during checkpoint sync. |
+| Apollo optimizer | train_router._optimizer | ~2.5GB for rank-64. Persisted to `/tmp/apollo_optimizer_state.pt` during checkpoint sync. |
 | HF model with vLLM views | train_router._model | Lazy-loaded on first /train. Parameters point to vLLM's GPU memory. |
 
 ## Hyperparameters
@@ -237,7 +236,7 @@ a few hundred MB.
 | Parameter | Value | Rationale |
 |-----------|-------|-----------|
 | Learning rate | 1e-5 to 1e-4 | Standard for full fine-tuning. Higher for diverse batches. |
-| Rank | 256 | Captures gradient structure across 100+ examples. ~10GB state. |
+| Rank | 64 | Captures gradient structure. ~2.5GB state. Defined in `train_router.DEFAULT_RANK`. |
 | Scale type | channel | Per-channel precision, matches LLaMA-Factory defaults. |
 | Epochs | 1 | One pass over diverse data. Multiple epochs risk overfitting. |
 | Batch size | 1 | Single examples, immediate updates. |
@@ -248,7 +247,7 @@ a few hundred MB.
 ## Components
 
 ### Built ✓
-- `optimizer.py` — Apollo optimizer (configurable rank, default 256)
+- `optimizer.py` — Apollo optimizer (configurable rank)
 - `train_router.py` — /train endpoint, runs in vLLM process
 - `weight_mapping.py` — vLLM merged → HF separate views (validated)
 - `export_hook.py` — vLLM plugin hook for IPC handle export

training/apollo_plugin/optimizer.py

@@ -8,9 +8,9 @@ Channel-wise or tensor-wise scaling is sufficient. Apollo approximates
 these scaling factors using a low-rank auxiliary optimizer state based on
 pure random projection.
 
-Default rank=256 (full Apollo). ~10GB state for 27B model, <0.25%
-compute overhead vs forward+backward. Captures gradient structure
-across 100+ behavioral training examples per batch.
+Default rank=64. ~2.5GB state for 27B model, <0.25% compute overhead
+vs forward+backward. Sufficient for behavioral training with diverse
+examples.
 
 Key implementation details from the paper:
   - Gradient scale factor α = √(n/r) compensates for projection ratio
@@ -34,7 +34,7 @@ class Apollo(Optimizer):
     Args:
         params: model parameters
         lr: learning rate (default: 1e-4)
-        rank: projection rank (default: 256)
+        rank: projection rank (default: 64)
         betas: Adam momentum coefficients (default: (0.9, 0.999))
         eps: numerical stability term (default: 1e-8)
         weight_decay: decoupled weight decay (default: 0.01)
@@ -46,7 +46,7 @@ class Apollo(Optimizer):
             Set to None to disable.
     """
 
-    def __init__(self, params, lr=1e-4, rank=256, betas=(0.9, 0.999),
+    def __init__(self, params, lr=1e-4, rank=64, betas=(0.9, 0.999),
                  eps=1e-8, weight_decay=0.01, warmup_steps=0,
                  scale=None, proj_refresh=200, norm_growth_limit=1.01):
         defaults = dict(lr=lr, rank=rank, betas=betas, eps=eps,

training/apollo_plugin/train_router.py

@@ -42,6 +42,7 @@ _initialized: bool = False
 _optimizer: Any = None  # Persisted Apollo optimizer
 
 OPTIMIZER_STATE_PATH = "/tmp/apollo_optimizer_state.pt"
+DEFAULT_RANK = 64
 
 
 def _load_training_model() -> nn.Module:
@@ -150,7 +151,7 @@ def _get_or_create_optimizer(model: nn.Module, config: dict[str, Any]):
     apollo_params, standard_params = [], []
     for p in model.parameters():
         if p.requires_grad:
-            if p.ndim >= 2 and min(p.shape) >= 256:
+            if p.ndim >= 2 and min(p.shape) >= DEFAULT_RANK:
                 apollo_params.append(p)
             else:
                 standard_params.append(p)
@@ -168,7 +169,7 @@ def _get_or_create_optimizer(model: nn.Module, config: dict[str, Any]):
     _optimizer = Apollo(
         groups,
         lr=config.get('lr', 1e-5),
-        rank=config.get('rank', 256),
+        rank=config.get('rank', DEFAULT_RANK),
         betas=tuple(config.get('betas', (0.9, 0.999))),
         eps=config.get('eps', 1e-8),
         weight_decay=config.get('weight_decay', 0.01),