[Bug]: vllm 0.19.0, gemma4, The format of the tool call returned by vllm is incorrect.

[Honghe]

Your current environment

The output of python collect_env.py

==============================
        System Info
==============================
OS                           : Ubuntu 24.04.4 LTS (x86_64)
GCC version                  : (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0
Clang version                : Could not collect
CMake version                : Could not collect
Libc version                 : glibc-2.39

==============================
       PyTorch Info
==============================
PyTorch version              : 2.10.0+cu130
Is debug build               : False
CUDA used to build PyTorch   : 13.0
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.13 (main, Mar 20 2026, 00:33:26) [Clang 22.1.1 ] (64-bit runtime)
Python platform              : Linux-6.8.0-107-generic-x86_64-with-glibc2.39

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.9.86
CUDA_MODULE_LOADING set to   :
GPU models and configuration :
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090
GPU 2: NVIDIA GeForce RTX 4090
GPU 3: NVIDIA GeForce RTX 4090

Nvidia driver version        : 590.48.01
cuDNN version                : Could not collect
HIP runtime version          : N/A
MIOpen runtime version       : N/A
Is XNNPACK available         : True

==============================
          CPU Info
==============================
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  256
On-line CPU(s) list:                     0-255
Vendor ID:                               AuthenticAMD
Model name:                              AMD EPYC 7B13 64-Core Processor
CPU family:                              25
Model:                                   1
Thread(s) per core:                      2
Core(s) per socket:                      64
Socket(s):                               2
Stepping:                                1
Frequency boost:                         enabled
CPU(s) scaling MHz:                      45%
CPU max MHz:                             3541.3679
CPU min MHz:                             1500.0000
BogoMIPS:                                4491.45
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin brs arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm debug_swap ibpb_exit_to_user
Virtualization:                          AMD-V
L1d cache:                               4 MiB (128 instances)
L1i cache:                               4 MiB (128 instances)
L2 cache:                                64 MiB (128 instances)
L3 cache:                                512 MiB (16 instances)
NUMA node(s):                            2
NUMA node0 CPU(s):                       0-63,128-191
NUMA node1 CPU(s):                       64-127,192-255
Vulnerability Gather data sampling:      Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Not affected
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Not affected
Vulnerability Spec rstack overflow:      Mitigation; Safe RET
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Vulnerable: Clear CPU buffers attempted, no microcode
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Mitigation; IBPB before exit to userspace

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.6.6
[pip3] numpy==2.4.4
[pip3] nvidia-cublas==13.1.0.3
[pip3] nvidia-cuda-cupti==13.0.85
[pip3] nvidia-cuda-nvrtc==13.0.88
[pip3] nvidia-cuda-runtime==13.0.96
[pip3] nvidia-cudnn-cu13==9.15.1.9
[pip3] nvidia-cudnn-frontend==1.18.0
[pip3] nvidia-cufft==12.0.0.61
[pip3] nvidia-cufile==1.15.1.6
[pip3] nvidia-curand==10.4.0.35
[pip3] nvidia-cusolver==12.0.4.66
[pip3] nvidia-cusparse==12.6.3.3
[pip3] nvidia-cusparselt-cu13==0.8.0
[pip3] nvidia-cutlass-dsl==4.4.2
[pip3] nvidia-cutlass-dsl-libs-base==4.4.2
[pip3] nvidia-ml-py==13.595.45
[pip3] nvidia-nccl-cu13==2.28.9
[pip3] nvidia-nvjitlink==13.0.88
[pip3] nvidia-nvshmem-cu13==3.4.5
[pip3] nvidia-nvtx==13.0.85
[pip3] pyzmq==27.1.0
[pip3] torch==2.10.0+cu130
[pip3] torch_c_dlpack_ext==0.1.5
[pip3] torchaudio==2.10.0+cu130
[pip3] torchvision==0.25.0+cu130
[pip3] transformers==5.5.3
[pip3] triton==3.6.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.19.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NODE    SYS     SYS     0-63,128-191    0               N/A
GPU1    NODE     X      SYS     SYS     0-63,128-191    0               N/A
GPU2    SYS     SYS      X      NODE    64-127,192-255  1               N/A
GPU3    SYS     SYS     NODE     X      64-127,192-255  1               N/A

Legend:

  X    = Self
  SYS  = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)
  NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node
  PHB  = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)
  PXB  = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)
  PIX  = Connection traversing at most a single PCIe bridge
  NV#  = Connection traversing a bonded set of # NVLinks

==============================
     Environment Variables
==============================
LD_LIBRARY_PATH=/usr/local/cuda/lib64:
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_ubuntu

🐛 Describe the bug

the output tool-call content:
the json string is surrounded with specific chars <|":

{
    "pdfs": [
        "<|"|"/home/h/.openclaw/workspace-kimiclaw/pdfs/a.pdf<|"|", "<|"|"/home/h/.openclaw/workspace-kimiclaw/pdfs/b.pdf<|"|"
    ],
    "prompt": "analyse these 2 pdfs."
}

the second format error case:

{
  "id": "chatcmpl-b327d53b9cd3dfc8",
  "object": "chat.completion",
  "created": 1776009391,
  "model": "RedHatAI/gemma-4-31B-it-FP8_BLOCK",
  "usage": null,
  "choices": [
    {
      "index": 0,
      "finish_reason": "stop",
      "role": "assistant",
      "content": "<|tool_call>call:process{action:<|\"|>poll<|\"|>,sessionId:<|\"|>calm-ridge<|\"|>,timeout:20000}<tool_call|>",
      "stop_reason": 50
    }
  ]
}

start command:

VLLM_USE_MODELSCOPE=true \
vllm serve google/gemma-4-31B-it \
--port 8000 \
--tensor-parallel-size 4 \
--max-model-len 160000 \
--reasoning-parser gemma4 \
--enable-auto-tool-choice \
--tool-call-parser gemma4 \
--kv-cache-dtype fp8 \
--gpu-memory-utilization 0.92 \
--max-num-seqs 2

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[MajestixAi]

You quantized the model incorrectly.
You need to make sure you pull the latest model from HuggingFace, and then use the vLLM --quantization flag to quantize the model on the fly. The bug goes away. Use the vLLM gemma4 image.

[MajestixAi]

[
  "--disable-custom-all-reduce",
  "--trust-remote-code",
  "--quantization",
  "fp8",
  "--gpu-memory-utilization",
  " 0.95",
  "--max-model-len",
  "262144",
  "--kv-cache-dtype",
  "fp8",
  "--enable-auto-tool-choice",
  "--tool-call-parser",
  "gemma4",
  "--reasoning-parser",
  "gemma4"
]

[github-actions]

This issue has been automatically marked as stale because it has not had any activity within 90 days. It will be automatically closed if no further activity occurs within 30 days. Leave a comment if you feel this issue should remain open. Thank you!

[gcavalcante8808]

Using the google/gemma-4-E4B-it-qat-w4a16-ct model version with VLLM 0.23.0, it seems that Gemma emitted a direct tool call (<|tool_call>, token 48), but vLLM’s reasoning parser only ends reasoningon <channel|> (token 101). It therefore never hands output to the tool parser and streams the raw syntax asdelta.content. My personal harness correctly displays that as text.

As such, if I deactivate the reasoning parser, the tool parser works as intended.

I'll try to test with 0.26.0 and report the result.