| # vLLM Version and Per-Model Launch Notes |
|
|
| **Version used in paper:** vLLM 0.19.0 |
|
|
| All five models are served via `vllm.entrypoints.openai.api_server`. Common |
| defaults across all models: |
|
|
| | Parameter | Value | |
| |-----------|-------| |
| | `--max-model-len` | 16384 | |
| | `--gpu-memory-utilization` | 0.90 | |
| | `--enable-auto-tool-choice` | (flag) | |
| | `--no-scheduler-reserve-full-isl` | (flag, required — avoids a vLLM 0.19.0 scheduler deadlock under sustained multi-agent load) | |
| | Input context cap | 12,000 tokens (set in runner via `_input_token_limit`) | |
| | Max output tokens | 1536 (512 for Gemma) | |
| | Temperature | 0.85 | |
| | BF16 weights | default | |
|
|
| --- |
|
|
| ## Per-model deviations |
|
|
| ### Qwen/Qwen3-32B |
| - TP=2 (GPUs 0-1) |
| - `--tool-call-parser hermes` |
| - `tool_choice=required` |
| - Thinking suppressed via `chat_template_kwargs = {"enable_thinking": False}` |
|
|
| ### openai/gpt-oss-20b |
| - TP=1 (GPU 2); mxfp4 quantized weights fit in ~40 GB |
| - `--tool-call-parser openai` |
| - `--reasoning-parser openai_gptoss` |
| - `tool_choice=required` |
|
|
| ### mistralai/Magistral-Small-2509 |
| - TP=2 (GPUs 3-4) |
| - `--tokenizer-mode mistral --config-format mistral --load-format mistral` |
| (Mistral models require their own loader; standard HuggingFace format fails) |
| - `--tool-call-parser mistral` |
| - `--reasoning-parser mistral` |
| - `tool_choice=auto` — **Magistral returns empty `tool_calls` with `required`**; |
| `auto` is the correct setting |
| |
| ### zai-org/GLM-4-32B-0414 |
| - TP=2 (GPUs 5-6) |
| - `--tool-call-parser hermes` |
| - `tool_choice=required` — **GLM-4 silently returns plain text with `auto`**; |
| this was discovered when warm-up showed only 1/10 agents posting per run |
| |
| ### google/gemma-4-31B-it |
| - TP=2 (GPUs 0-1 or 3-4 depending on node configuration) |
| - `--tool-call-parser gemma4` |
| - `--chat-template oasis/tool_chat_template_gemma4.jinja` (custom template, required) |
| - `tool_choice=auto` — same symptom as Magistral with `required` |
| - `--max-num-seqs 64` — tuned to avoid KV-cache saturation under concurrent load |
| - `max_tokens=512` (runner-side cap, lower than other models) |
| - **Note:** There is a discrepancy between the paper appendix (max_num_seqs=64) |
| and an earlier production launch script (max_num_seqs=8). The appendix value |
| (64) is the final setting used in paper runs. |
| |
| --- |
| |
| ## Known issues in vLLM 0.19.0 |
| |
| - **Scheduler deadlock**: Under sustained multi-agent load, the scheduler can |
| deadlock. Mitigated by `--no-scheduler-reserve-full-isl` on all models. |
| - **Gemma KV-cache saturation**: Default scheduling settings cause OOM-like |
| behavior under concurrent requests. `--max-num-seqs 64` and ensuring |
| `--gpu-memory-utilization 0.90` resolves this. |
| - **Magistral/Gemma tool_choice=required**: Both models return empty |
| `tool_calls` lists when `tool_choice=required`; the runner uses `auto` and |
| handles non-tool responses with prose-to-action scaffolding. |
|
|
