vllm - 💡(How to fix) Fix [Usage]: serve部署模型后，调用chat.completions输入给模型的text中image_pad token被提到了prompt开头 [1 participants]

vllm2026-03-20 10:59:00

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vllm-project/vllm#37674•Fetched 2026-04-08 01:04:08

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NormanWhc

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Code Example

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version                : Could not collect
CMake version                : version 4.1.0
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.8.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.11 (main, Jun  4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-4.18.0-348.7.1.el8_5.x86_64-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.8.93
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration : 
GPU 0: NVIDIA H20Z
GPU 1: NVIDIA H20Z
GPU 2: NVIDIA H20Z
GPU 3: NVIDIA H20Z

Nvidia driver version        : 550.144.03
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:                   46 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          180
On-line CPU(s) list:             0-179
Vendor ID:                       GenuineIntel
Model name:                      INTEL(R) XEON(R) PLATINUM 8558
CPU family:                      6
Model:                           207
Thread(s) per core:              2
Core(s) per socket:              45
Socket(s):                       2
Stepping:                        2
BogoMIPS:                        4200.00
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk avx512_fp16 arch_capabilities
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       4.2 MiB (90 instances)
L1i cache:                       2.8 MiB (90 instances)
L2 cache:                        180 MiB (90 instances)
L3 cache:                        520 MiB (2 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-179
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:        Vulnerable, IBPB: disabled, STIBP: disabled
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.3.1
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.14.1
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pynvml==12.0.0
[pip3] pyzmq==27.1.0
[pip3] torch==2.8.0+cu128
[pip3] torchaudio==2.8.0+cu128
[pip3] torchcodec==0.7.0
[pip3] torchvision==0.23.0+cu128
[pip3] transformers==4.57.6
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.4.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.11.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6    NIC7    NIC8    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    SYS     PIX     PHB     PHB     PHB     SYS     SYS     SYS     SYS     0-179   0               N/A
GPU1    NV18     X      NV18    NV18    SYS     PHB     PIX     PHB     PHB     SYS     SYS     SYS     SYS     0-179   0               N/A
GPU2    NV18    NV18     X      NV18    SYS     PHB     PHB     PIX     PHB     SYS     SYS     SYS     SYS     0-179   0               N/A
GPU3    NV18    NV18    NV18     X      SYS     PHB     PHB     PHB     PIX     SYS     SYS     SYS     SYS     0-179   0               N/A
NIC0    SYS     SYS     SYS     SYS      X      SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS
NIC1    PIX     PHB     PHB     PHB     SYS      X      PHB     PHB     PHB     SYS     SYS     SYS     SYS
NIC2    PHB     PIX     PHB     PHB     SYS     PHB      X      PHB     PHB     SYS     SYS     SYS     SYS
NIC3    PHB     PHB     PIX     PHB     SYS     PHB     PHB      X      PHB     SYS     SYS     SYS     SYS
NIC4    PHB     PHB     PHB     PIX     SYS     PHB     PHB     PHB      X      SYS     SYS     SYS     SYS
NIC5    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS      X      PHB     PHB     PHB
NIC6    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB      X      PHB     PHB
NIC7    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB     PHB      X      PHB
NIC8    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB     PHB     PHB      X 

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

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=void
NVIDIA_REQUIRE_CUDA=cuda>=12.8 brand=unknown,driver>=470,driver<471 brand=grid,driver>=470,driver<471 brand=tesla,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=vapps,driver>=470,driver<471 brand=vpc,driver>=470,driver<471 brand=vcs,driver>=470,driver<471 brand=vws,driver>=470,driver<471 brand=cloudgaming,driver>=470,driver<471 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566
NCCL_VERSION=2.25.1-1
NVIDIA_DRIVER_CAPABILITIES=compute,video,utility
NCCL_DEBUG=INFO
NVIDIA_PRODUCT_NAME=CUDA
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.8.1
CUDA_VISIBLE_DEVICES=0,1,2,3
CUDA_VISIBLE_DEVICES=0,1,2,3
LD_LIBRARY_PATH=/usr/local/cuda/lib64
NVIDIA_CTK_LIBCUDA_DIR=/usr/lib64
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

RAW_BUFFERClick to expand / collapse

Your current environment

输入的messages为[{'type':'text','text': '这是一条小狗'},{"type": "image_url","image_url": {"url": f"data:{mime};base64,{b64}"}},{'type': 'text', 'text': '真可爱'}]}]

vllm返回的prompt_token_id前几个为：prompt_token_ids=[151644, 872, 198, 151652, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655]，与输入不符

有什么解决办法吗？

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version                : Could not collect
CMake version                : version 4.1.0
Libc version                 : glibc-2.35

==============================
       PyTorch Info
==============================
PyTorch version              : 2.8.0+cu128
Is debug build               : False
CUDA used to build PyTorch   : 12.8
ROCM used to build PyTorch   : N/A

==============================
      Python Environment
==============================
Python version               : 3.12.11 (main, Jun  4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)
Python platform              : Linux-4.18.0-348.7.1.el8_5.x86_64-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.8.93
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration : 
GPU 0: NVIDIA H20Z
GPU 1: NVIDIA H20Z
GPU 2: NVIDIA H20Z
GPU 3: NVIDIA H20Z

Nvidia driver version        : 550.144.03
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:                   46 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          180
On-line CPU(s) list:             0-179
Vendor ID:                       GenuineIntel
Model name:                      INTEL(R) XEON(R) PLATINUM 8558
CPU family:                      6
Model:                           207
Thread(s) per core:              2
Core(s) per socket:              45
Socket(s):                       2
Stepping:                        2
BogoMIPS:                        4200.00
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b fsrm md_clear serialize tsxldtrk avx512_fp16 arch_capabilities
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       4.2 MiB (90 instances)
L1i cache:                       2.8 MiB (90 instances)
L2 cache:                        180 MiB (90 instances)
L3 cache:                        520 MiB (2 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-179
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:        Vulnerable, IBPB: disabled, STIBP: disabled
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.3.1
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.14.1
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.13.1.3
[pip3] nvidia-curand-cu12==10.3.9.90
[pip3] nvidia-cusolver-cu12==11.7.3.90
[pip3] nvidia-cusparse-cu12==12.5.8.93
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-ml-py==12.575.51
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvshmem-cu12==3.4.5
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pynvml==12.0.0
[pip3] pyzmq==27.1.0
[pip3] torch==2.8.0+cu128
[pip3] torchaudio==2.8.0+cu128
[pip3] torchcodec==0.7.0
[pip3] torchvision==0.23.0+cu128
[pip3] transformers==4.57.6
[pip3] transformers-stream-generator==0.0.5
[pip3] triton==3.4.0
[conda] Could not collect

==============================
         vLLM Info
==============================
ROCM Version                 : Could not collect
vLLM Version                 : 0.11.0
vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
        GPU0    GPU1    GPU2    GPU3    NIC0    NIC1    NIC2    NIC3    NIC4    NIC5    NIC6    NIC7    NIC8    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      NV18    NV18    NV18    SYS     PIX     PHB     PHB     PHB     SYS     SYS     SYS     SYS     0-179   0               N/A
GPU1    NV18     X      NV18    NV18    SYS     PHB     PIX     PHB     PHB     SYS     SYS     SYS     SYS     0-179   0               N/A
GPU2    NV18    NV18     X      NV18    SYS     PHB     PHB     PIX     PHB     SYS     SYS     SYS     SYS     0-179   0               N/A
GPU3    NV18    NV18    NV18     X      SYS     PHB     PHB     PHB     PIX     SYS     SYS     SYS     SYS     0-179   0               N/A
NIC0    SYS     SYS     SYS     SYS      X      SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS
NIC1    PIX     PHB     PHB     PHB     SYS      X      PHB     PHB     PHB     SYS     SYS     SYS     SYS
NIC2    PHB     PIX     PHB     PHB     SYS     PHB      X      PHB     PHB     SYS     SYS     SYS     SYS
NIC3    PHB     PHB     PIX     PHB     SYS     PHB     PHB      X      PHB     SYS     SYS     SYS     SYS
NIC4    PHB     PHB     PHB     PIX     SYS     PHB     PHB     PHB      X      SYS     SYS     SYS     SYS
NIC5    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS      X      PHB     PHB     PHB
NIC6    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB      X      PHB     PHB
NIC7    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB     PHB      X      PHB
NIC8    SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     SYS     PHB     PHB     PHB      X 

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

NIC Legend:

  NIC0: mlx5_0
  NIC1: mlx5_1
  NIC2: mlx5_2
  NIC3: mlx5_3
  NIC4: mlx5_4
  NIC5: mlx5_5
  NIC6: mlx5_6
  NIC7: mlx5_7
  NIC8: mlx5_8

==============================
     Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=void
NVIDIA_REQUIRE_CUDA=cuda>=12.8 brand=unknown,driver>=470,driver<471 brand=grid,driver>=470,driver<471 brand=tesla,driver>=470,driver<471 brand=nvidia,driver>=470,driver<471 brand=quadro,driver>=470,driver<471 brand=quadrortx,driver>=470,driver<471 brand=nvidiartx,driver>=470,driver<471 brand=vapps,driver>=470,driver<471 brand=vpc,driver>=470,driver<471 brand=vcs,driver>=470,driver<471 brand=vws,driver>=470,driver<471 brand=cloudgaming,driver>=470,driver<471 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=565,driver<566 brand=grid,driver>=565,driver<566 brand=tesla,driver>=565,driver<566 brand=nvidia,driver>=565,driver<566 brand=quadro,driver>=565,driver<566 brand=quadrortx,driver>=565,driver<566 brand=nvidiartx,driver>=565,driver<566 brand=vapps,driver>=565,driver<566 brand=vpc,driver>=565,driver<566 brand=vcs,driver>=565,driver<566 brand=vws,driver>=565,driver<566 brand=cloudgaming,driver>=565,driver<566
NCCL_VERSION=2.25.1-1
NVIDIA_DRIVER_CAPABILITIES=compute,video,utility
NCCL_DEBUG=INFO
NVIDIA_PRODUCT_NAME=CUDA
VLLM_USAGE_SOURCE=production-docker-image
CUDA_VERSION=12.8.1
CUDA_VISIBLE_DEVICES=0,1,2,3
CUDA_VISIBLE_DEVICES=0,1,2,3
LD_LIBRARY_PATH=/usr/local/cuda/lib64
NVIDIA_CTK_LIBCUDA_DIR=/usr/lib64
CUDA_HOME=/usr/local/cuda
CUDA_HOME=/usr/local/cuda
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

How would you like to use vllm

I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.

Before submitting a new issue...

Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the documentation page, which can answer lots of frequently asked questions.

extent analysis

Fix Plan

To integrate the specific model with vllm, follow these steps:

Check the model's compatibility with vllm by verifying its architecture and input/output formats.
Ensure the model is properly installed and imported in the Python environment.
Use the vllm library to load the model and create an inference pipeline.

Example code:

import torch
from vllm import VLLM

# Load the model
model = torch.load('path/to/model.pth')

# Create a vllm instance
vllm = VLLM(model)

# Define the input data
input_data = [{'type': 'text', 'text': '这是一条小狗'}, 
              {"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,..."}}]

# Run inference
output = vllm(input_data)

# Print the output
print(output)

Verification

To verify the fix, check the output of the inference pipeline and ensure it matches the expected results.

Extra Tips

Make sure to handle any exceptions or errors that may occur during the inference process.
Consider optimizing the model and inference pipeline for better performance.
Refer to the vllm documentation for more information on integrating models and running inference.

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vllm - 💡(How to fix) Fix [Usage]: serve部署模型后，调用chat.completions输入给模型的text中image_pad token被提到了prompt开头 [1 participants]

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