[Usage]: After starting the QwQ-32B model normally, it was found that the model could not output the thought tag normally

[shatang123]

Your current environment

INFO 03-08 00:00:39 init.py:190] Automatically detected platform cuda.
Collecting environment information...
PyTorch version: 2.5.1+cu124
Is debug build: False
CUDA used to build PyTorch: 12.4
ROCM used to build PyTorch: N/A

OS: CentOS Linux release 7.9.2009 (Core) (x86_64)
GCC version: (GCC) 11.2.0
Clang version: Could not collect
CMake version: version 3.31.5
Libc version: glibc-2.17

Python version: 3.12.9 | packaged by Anaconda, Inc. | (main, Feb 6 2025, 18:56:27) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-3.10.0-1160.92.1.el7.x86_64-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A10
GPU 1: NVIDIA A10
GPU 2: NVIDIA A10
GPU 3: NVIDIA A10
GPU 4: NVIDIA A10
GPU 5: NVIDIA A10
GPU 6: NVIDIA A10
GPU 7: NVIDIA A10

Nvidia driver version: 550.127.08
cuDNN version: Probably one of the following:
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_adv.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_cnn.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_engines_precompiled.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_engines_runtime_compiled.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_graph.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_heuristic.so.9.2.0
/usr/local/cuda-12.4/targets/x86_64-linux/lib/libcudnn_ops.so.9.2.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 128
On-line CPU(s) list: 0-127
Thread(s) per core: 2
Core(s) per socket: 32
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 106
Model name: Intel(R) Xeon(R) Platinum 8369B CPU @ 2.90GHz
Stepping: 6
CPU MHz: 3499.859
CPU max MHz: 3500.0000
CPU min MHz: 800.0000
BogoMIPS: 5800.00
Virtualization: VT-x
L1d cache: 48K
L1i cache: 32K
L2 cache: 1280K
L3 cache: 49152K
NUMA node0 CPU(s): 0-31,64-95
NUMA node1 CPU(s): 32-63,96-127
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc aperfmperf eagerfpu pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch epb cat_l3 invpcid_single ssbd mba rsb_ctxsw ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq md_clear pconfig spec_ctrl intel_stibp flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.4.5.8
[pip3] nvidia-cuda-cupti-cu12==12.4.127
[pip3] nvidia-cuda-nvrtc-cu12==12.4.127
[pip3] nvidia-cuda-runtime-cu12==12.4.127
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.2.1.3
[pip3] nvidia-curand-cu12==10.3.5.147
[pip3] nvidia-cusolver-cu12==11.6.1.9
[pip3] nvidia-cusparse-cu12==12.3.1.170
[pip3] nvidia-cusparselt-cu12==0.6.2
[pip3] nvidia-ml-py==12.570.86
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.4.127
[pip3] nvidia-nvtx-cu12==12.4.127
[pip3] pyzmq==26.2.1
[pip3] torch==2.5.1
[pip3] torchaudio==2.5.1
[pip3] torchvision==0.20.1
[pip3] transformers==4.49.0
[pip3] triton==3.1.0
[conda] numpy 1.26.4 pypi_0 pypi
[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi
[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi
[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi
[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi
[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi
[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi
[conda] nvidia-cusparselt-cu12 0.6.2 pypi_0 pypi
[conda] nvidia-ml-py 12.570.86 pypi_0 pypi
[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi
[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi
[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi
[conda] pyzmq 26.2.1 pypi_0 pypi
[conda] torch 2.5.1 pypi_0 pypi
[conda] torchaudio 2.5.1 pypi_0 pypi
[conda] torchvision 0.20.1 pypi_0 pypi
[conda] transformers 4.49.0 pypi_0 pypi
[conda] triton 3.1.0 pypi_0 pypi
ROCM Version: Could not collect
Neuron SDK Version: N/A
vLLM Version: 0.7.2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PIX NODE NODE SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU1 PIX X NODE NODE SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU2 NODE NODE X PIX SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU3 NODE NODE PIX X SYS SYS SYS SYS 0-31,64-95 0 N/A
GPU4 SYS SYS SYS SYS X PIX NODE NODE 32-63,96-127 1 N/A
GPU5 SYS SYS SYS SYS PIX X NODE NODE 32-63,96-127 1 N/A
GPU6 SYS SYS SYS SYS NODE NODE X PIX 32-63,96-127 1 N/A
GPU7 SYS SYS SYS SYS NODE NODE PIX X 32-63,96-127 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

CUDA_PATH=/usr/local/cuda-12.4
LD_LIBRARY_PATH=/root/anaconda3/envs/vllm/lib/python3.12/site-packages/cv2/../../lib64:/usr/local/cuda/lib64:/usr/local/lib64:/usr/local/cuda-12.4/lib64:
NCCL_CUMEM_ENABLE=0
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

The call example is as follows, the model normally replies to the content, you can see that there is thinking content, but there is no thinking label

I don't know if this is a feature of the model or a vllm problem, but I remember wrapping it with the tag

Before submitting a new issue...

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[jeejeelee]

This is due to QWQ chat template contains <think>, as shown in your first cut image

[Superskyyy]

It works normally on my side, even the template contains think token it outputs normally. Using the deepseek parser.

[shatang123]

This is due to QWQ chat template contains <think>, as shown in your first cut image

I see the official chat_template content, which does add the start >assistant\n\n. I tried using --enable-reasoning --reasoning-parser deepseek_r1, but the thinking content didn't show up in reasoning_content

[0-0ARK]

I also encountered this problem, any solutions or advise?
I am using python -m vllm.entrypoints.openai.api_server --model ./QwQ-32B-AWQ --max-model-len 3600 --enable-reasoning --reasoning-parser deepseek_r1

[gaocegege]

/cc @WangErXiao

I cannot reproduce it. Our reasoning support is compatible with <think>...</think> and ...</think> by design. It should work well with the QwQ chat template.

[gaocegege]

It has the <think> in the tokenizer config https://modelscope.cn/models/Qwen/QwQ-32B-AWQ/file/view/master?fileName=tokenizer_config.json&status=1#L230

Then is it a bug from AWQ model tokenizer?

[gaocegege]

I found the tokens in https://modelscope.cn/models/Qwen/QwQ-32B-AWQ/file/view/master?fileName=added_tokens.json&status=1

[WangErXiao]

I just tested QwQ-32B-AWQ and QwQ-32B with --reasoning-parser deepseek_r1 using v0.7.3, they are all ok. @gaocegege