v0.8.0:3 - add --max-num-batched-tokens=16384 to vision models (gemma4, qwen3-vl)

After the recent eugr/spark-vllm-docker update, vLLM became stricter about multimodal token budgets:

  ValueError: Chunked MM input disabled but max_tokens_per_mm_item (2496) is
  larger than max_num_batched_tokens (2048). Please increase max_num_batched_tokens.

Each image input produces 2496 tokens, but vLLM's default --max-num-batched-tokens of 2048 is just under. Same class of bug as the Qwen3.6 Mamba block-size assertion we fixed in 0.6.0:1, surfacing on different models.

Fix: bake --max-num-batched-tokens=16384 into every multimodal model entry. Now applied to:
  - qwen36 (already had it for the Mamba constraint; works for multimodal too since Qwen3.6 has vision)
  - gemma4 (crashed today on engine init)
  - qwen3-vl (would crash with the same error if anyone tried it)

The pre-flight Test button validates argparse but the 2048<2496 check happens at runtime engine init, so it's not caught by Test — only by actually trying to load. This is exactly the kind of bug v0.7's Test catches the *syntax* of but not the *semantics*; runtime errors like this still surface only on real swap. Known limitation documented in v0.7 release notes.

image/models.yaml

@@ -30,6 +30,7 @@ models:
       - -tp=2
       - --distributed-executor-backend=ray
       - --max-model-len=32768
+      - --max-num-batched-tokens=16384
 
   gemma4:
     display_name: "Gemma 4 31B"
@@ -45,6 +46,7 @@ models:
     vllm_args:
       - --gpu-memory-utilization=0.8
       - --max-model-len=32768
+      - --max-num-batched-tokens=16384
       - --reasoning-parser=gemma4
       - --tool-call-parser=gemma4
       - --enable-auto-tool-choice