Phase 0: model migration (qwen3.5-9b-vlm → gemma-4-12b), context alignment (all 262K), routing tiers + MoE spillover, fix dashboard window=1h parsing

router/router.py

@@ -10,31 +10,31 @@ GPU_LIGHT_URL = os.environ.get("GPU_LIGHT_URL", "http://192.168.68.110:8080/v1")
 GPU_SIDECARS = {
     "qwen3.6-35B-A3B": "http://192.168.68.15:8090",
     "qwen3.6-27B-code": "http://192.168.68.8:8090",
-    "qwen3.5-9b-vlm": "http://192.168.68.110:8090",
+    "gemma-4-12b": "http://192.168.68.110:8090",
 }
 GPU_URLS = {
     "qwen3.6-35B-A3B": GPU_MOE_URL,
     "qwen3.6-27B-code": GPU_DENSE_URL,
-    "qwen3.5-9b-vlm": GPU_LIGHT_URL,
+    "gemma-4-12b": GPU_LIGHT_URL,
 }
 # Max concurrent requests per GPU (based on llama.cpp --parallel)
 GPU_MAX_CONCURRENT = {
     "qwen3.6-35B-A3B": 2,   # 2 slots (cross-agent spread prevents overheating)
     "qwen3.6-27B-code": 2,  # 2 slots (128K context frees VRAM)
-    "qwen3.5-9b-vlm": 2,       # 2 slots (12GB VRAM, 4GB headroom)
+    "gemma-4-12b": 2,       # 2 slots (7.1GB VRAM)
 }
 
 # Context window sizes (tokens) — used for compaction signals
 GPU_CONTEXT = {
     "qwen3.6-35B-A3B": 262144,
-    "qwen3.6-27B-code": 131072,
+    "qwen3.6-27B-code": 262144,
-    "qwen3.5-9b-vlm": 262144,
+    "gemma-4-12b": 262144,
 }
 
 TIER_MODELS = {
-    "starter": ["qwen3.5-9b-vlm"],
+    "starter": ["gemma-4-12b"],
-    "professional": ["qwen3.6-35B-A3B", "qwen3.6-27B-code", "qwen3.5-9b-vlm"],
+    "professional": ["qwen3.6-35B-A3B", "qwen3.6-27B-code", "gemma-4-12b"],
-    "enterprise": ["qwen3.6-35B-A3B", "qwen3.6-27B-code", "qwen3.5-9b-vlm"],
+    "enterprise": ["qwen3.6-35B-A3B", "qwen3.6-27B-code", "gemma-4-12b"],
 }
 # API keys loaded from API_KEYS env var (set in docker-compose.yml)
 # Fallback is dev-only — production MUST set API_KEYS env var
@@ -216,74 +216,100 @@ def select_best_gpu(candidates, reason, agent=""):
         return {"model": best, "reason": "load_balanced_" + reason}
     return None
 
+
+
 def route(rd, tier, agent=""):
     msgs = rd.get("messages",[]); t = estimate_tokens(msgs)
     sys = any(m.get("role")=="system" for m in msgs)
     turns = len([m for m in msgs if m.get("role") in ("user","assistant")])
     hints = rd.get("routing_hints",{})
-    allowed = TIER_MODELS.get(tier, ["qwen3.5-9b-vlm"])
+    allowed = TIER_MODELS.get(tier, ["gemma-4-12b"])
     avail = [m for m in available_models() if m in allowed]
     if not avail: return {"model": allowed[0], "reason": "all_saturated", "saturated": True}
-    # Check if all available GPUs are at max capacity
     if all(is_gpu_busy(m) for m in avail):
         return {"model": avail[0], "reason": "all_saturated", "saturated": True}
-    
+
+    # GUARD: multimodal -> VLM only (sole vision model)
+    has_image = any(
+        isinstance(m.get("content"), list) and
+        any(p.get("type") == "image_url" for p in m["content"] if isinstance(p, dict))
+        for m in msgs
+    )
+    if has_image:
+        if "gemma-4-12b" in avail and not is_gpu_busy("gemma-4-12b"):
+            return {"model": "gemma-4-12b", "reason": "vision"}
+        elif "gemma-4-12b" in avail:
+            return {"model": "gemma-4-12b", "reason": "vision_saturated", "saturated": True}
+        else:
+            return {"model": allowed[0], "reason": "vision_unavailable"}
+
     req = rd.get("model","auto")
     if req != "auto":
         target = req if req in avail else avail[0]
-        # If explicit model is busy, check if another can take it
         if is_gpu_busy(target) and req in allowed:
             alts = [m for m in avail if m != target and m in allowed]
             if alts:
                 alt = select_best_gpu(alts, "explicit", agent)
                 if alt: return alt
         return {"model": target, "reason": "explicit"}
-    
+
     if hints:
-        if hints.get("priority")=="speed" and "qwen3.5-9b-vlm" in avail:
+        if hints.get("priority")=="speed" and "gemma-4-12b" in avail:
-            return select_best_gpu(["qwen3.5-9b-vlm"], "hint_speed", agent) or {"model":"qwen3.5-9b-vlm","reason":"hint_speed"}
+            return select_best_gpu(["gemma-4-12b"], "hint_speed", agent) or {"model":"gemma-4-12b","reason":"hint_speed"}
         if hints.get("priority")=="quality" and "qwen3.6-35B-A3B" in avail:
             return select_best_gpu(["qwen3.6-35B-A3B"], "hint_quality", agent) or {"model":"qwen3.6-35B-A3B","reason":"hint_quality"}
-    
+        if hints.get("priority")=="code" and "qwen3.6-27B-code" in avail:
+            return select_best_gpu(["qwen3.6-27B-code"], "hint_code", agent) or {"model":"qwen3.6-27B-code","reason":"hint_code"}
+
     first_msg = msgs[0].get("content","") if msgs else ""
     words = len(first_msg.split()) if isinstance(first_msg, str) else 99
-    
+
-    # TIER 1: Lightweight — single-turn short queries → VLM (fastest)
+    # TIER 1: Tiny - single-turn micro queries -> VLM (fastest)
-    if not sys and turns <= 1 and t <= 500 and words <= 100 and "qwen3.5-9b-vlm" in avail:
+    if not sys and turns <= 1 and t <= 300 and words <= 100 and "gemma-4-12b" in avail:
-        if not is_gpu_busy("qwen3.5-9b-vlm"):
+        if not is_gpu_busy("gemma-4-12b"):
-            return {"model":"qwen3.5-9b-vlm","reason":"lightweight"}
+            return {"model":"gemma-4-12b","reason":"tiny"}
-        # VLM busy — Dense is faster for short queries than MoE
         fallback = [m for m in ["qwen3.6-27B-code","qwen3.6-35B-A3B"] if m in avail]
-        result = select_best_gpu(fallback, "lightweight_fallback", agent)
+        result = select_best_gpu(fallback, "tiny_fallback", agent)
         if result: return result
-    
+
-    # TIER 2: Simple conversations — VLM primary (up to 15K tok), fastest for moderate chat
+    # TIER 2: Light - moderate chat -> VLM first (fastest), Dense fallback
-    if t <= 15000 and turns <= 12 and "qwen3.5-9b-vlm" in avail:
+    if t <= 5000 and turns <= 4:
-        if not is_gpu_busy("qwen3.5-9b-vlm"):
+        candidates = [m for m in ["gemma-4-12b","qwen3.6-27B-code","qwen3.6-35B-A3B"] if m in avail]
-            return {"model":"qwen3.5-9b-vlm","reason":"simple_conv"}
+        result = select_best_gpu(candidates, "light", agent)
-        # VLM busy — fall back to Dense, then MoE
-        fallback = [m for m in ["qwen3.6-27B-code","qwen3.6-35B-A3B"] if m in avail]
-        result = select_best_gpu(fallback, "simple_conv_fallback", agent)
         if result: return result
-    
+
-    # TIER 3: Medium complexity — Dense primary, VLM fallback (quality + speed balance)
+    # TIER 3: Medium - quality matters -> MoE primary (60%), Dense spillover (40%)
-    if t <= 25000:
+    if t <= 30000:
-        candidates = [m for m in ["qwen3.6-27B-code","qwen3.5-9b-vlm","qwen3.6-35B-A3B"] if m in avail]
+        candidates = moe_spillover(avail, ["qwen3.6-35B-A3B","qwen3.6-27B-code","gemma-4-12b"])
         result = select_best_gpu(candidates, "medium", agent)
         if result: return result
-    
+
-    # TIER 4: Heavy reasoning — MoE primary (workhorse), Dense fallback
+    # TIER 4: Heavy - quality first -> Dense primary, MoE fallback
-    if t > 25000:
+    if t > 30000:
-        candidates = [m for m in ["qwen3.6-35B-A3B","qwen3.6-27B-code","qwen3.5-9b-vlm"] if m in avail]
+        candidates = [m for m in ["qwen3.6-27B-code","qwen3.6-35B-A3B","gemma-4-12b"] if m in avail]
-        result = select_best_gpu(candidates, "heavy_reasoning", agent)
+        result = select_best_gpu(candidates, "heavy", agent)
         if result: return result
-    
+
-    # TIER 5: Default — Dense primary, MoE fallback
+    # TIER 5: Default - MoE primary (60%), Dense spillover (40%)
-    candidates = [m for m in ["qwen3.6-27B-code","qwen3.5-9b-vlm","qwen3.6-35B-A3B"] if m in avail]
+    candidates = moe_spillover(avail, ["qwen3.6-35B-A3B","qwen3.6-27B-code","gemma-4-12b"])
     result = select_best_gpu(candidates, "default", agent)
     if result: return result
     return {"model":avail[0],"reason":"last_resort"}
 
+
+def moe_spillover(avail, default_order):
+    """Spill 40% of MoE-first traffic to Dense to prevent Strix Halo overheating.
+    Only applies when MoE is first candidate, available, and not busy."""
+    import random
+    if (default_order[0] == "qwen3.6-35B-A3B" 
+        and "qwen3.6-35B-A3B" in avail 
+        and not is_gpu_busy("qwen3.6-35B-A3B")
+        and "qwen3.6-27B-code" in avail
+        and not is_gpu_busy("qwen3.6-27B-code")
+        and random.random() < 0.4):
+        # Swap: Dense first, MoE second
+        return ["qwen3.6-27B-code","qwen3.6-35B-A3B"] + [m for m in default_order[2:] if m in avail and m not in ("qwen3.6-27B-code","qwen3.6-35B-A3B")]
+    return [m for m in default_order if m in avail]
 def clean_unicode(text):
     if not isinstance(text, str): return text
     text = text.replace(chr(0x2014), "-"); text = text.replace(chr(0x2013), "-")
@@ -494,7 +520,7 @@ def performance():
     """Per-request performance analytics with percentiles per model/reason/agent."""
     if not r: return jsonify({"error": "Redis unavailable"}), 503
     try:
-        window_hours = int(request.args.get("window", "24"))
+        window_hours = int(request.args.get("window", "24").replace("h",""))
         model_filter = request.args.get("model", "all")
 
         # Load recent records
@@ -611,7 +637,7 @@ def scatter():
     """Return individual data points for scatter plots (prompt_tokens vs latency)."""
     if not r: return jsonify({"error": "Redis unavailable"}), 503
     try:
-        window_hours = int(request.args.get("window", "24"))
+        window_hours = int(request.args.get("window", "24").replace("h",""))
         model_filter = request.args.get("model", "all")
         cutoff = time.time() - (window_hours * 3600)
         raw = r.lrange("perf:recent", 0, -1)