[Bug]: Qwen3-VL-8B-Thinking under vLLM shows inaccurate temporal localization compared to Transformers

[Xqle]

Your current environment

The output of python collect_env.py

Collecting environment information...
==============================
        System Info
==============================
OS                           : Ubuntu 22.04.5 LTS (x86_64)
GCC version                  : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version                : Could not collect
CMake version                : Could not collect
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               : 3.10.18 (main, Jun  5 2025, 13:14:17) [GCC 11.2.0] (64-bit runtime)
Python platform              : Linux-5.15.0-152-generic-x86_64-with-glibc2.35

==============================
       CUDA / GPU Info
==============================
Is CUDA available            : True
CUDA runtime version         : 12.4.99
CUDA_MODULE_LOADING set to   : LAZY
GPU models and configuration :
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090
GPU 4: NVIDIA GeForce RTX 3090
GPU 5: NVIDIA GeForce RTX 3090
GPU 6: NVIDIA GeForce RTX 3090
GPU 7: NVIDIA GeForce RTX 3090

Nvidia driver version        : 550.54.14
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, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  80
On-line CPU(s) list:                     0-79
Vendor ID:                               GenuineIntel
Model name:                              Intel(R) Xeon(R) Gold 6242R CPU @ 3.10GHz
CPU family:                              6
Model:                                   85
Thread(s) per core:                      2
Core(s) per socket:                      20
Socket(s):                               2
Stepping:                                7
CPU max MHz:                             4100.0000
CPU min MHz:                             1200.0000
BogoMIPS:                                6200.00
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 cpuid aperfmperf pni pclmulqdq dtes64 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 cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke avx512_vnni md_clear flush_l1d arch_capabilities
Virtualization:                          VT-x
L1d cache:                               1.3 MiB (40 instances)
L1i cache:                               1.3 MiB (40 instances)
L2 cache:                                40 MiB (40 instances)
L3 cache:                                71.5 MiB (2 instances)
NUMA node(s):                            2
NUMA node0 CPU(s):                       0-19,40-59
NUMA node1 CPU(s):                       20-39,60-79
Vulnerability Gather data sampling:      Mitigation; Microcode
Vulnerability Indirect target selection: Mitigation; Aligned branch/return thunks
Vulnerability Itlb multihit:             KVM: Mitigation: VMX disabled
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow:      Not affected
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Enhanced / Automatic IBRS; IBPB disabled; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop
Vulnerability Srbds:                     Not affected
Vulnerability Tsx async abort:           Mitigation; TSX disabled

==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.4.0rc3
[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==1.15.0
[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==13.580.82
[pip3] nvidia-nccl-cu12==2.27.3
[pip3] nvidia-nvjitlink-cu12==12.8.93
[pip3] nvidia-nvtx-cu12==12.8.90
[pip3] pyzmq==27.1.0
[pip3] torch==2.8.0
[pip3] torchaudio==2.8.0
[pip3] torchvision==0.23.0
[pip3] transformers==4.57.0
[pip3] triton==3.4.0
[conda] flashinfer-python         0.4.0rc3                 pypi_0    pypi
[conda] numpy                     2.2.6                    pypi_0    pypi
[conda] nvidia-cublas-cu12        12.8.4.1                 pypi_0    pypi
[conda] nvidia-cuda-cupti-cu12    12.8.90                  pypi_0    pypi
[conda] nvidia-cuda-nvrtc-cu12    12.8.93                  pypi_0    pypi
[conda] nvidia-cuda-runtime-cu12  12.8.90                  pypi_0    pypi
[conda] nvidia-cudnn-cu12         9.10.2.21                pypi_0    pypi
[conda] nvidia-cudnn-frontend     1.15.0                   pypi_0    pypi
[conda] nvidia-cufft-cu12         11.3.3.83                pypi_0    pypi
[conda] nvidia-cufile-cu12        1.13.1.3                 pypi_0    pypi
[conda] nvidia-curand-cu12        10.3.9.90                pypi_0    pypi
[conda] nvidia-cusolver-cu12      11.7.3.90                pypi_0    pypi
[conda] nvidia-cusparse-cu12      12.5.8.93                pypi_0    pypi
[conda] nvidia-cusparselt-cu12    0.7.1                    pypi_0    pypi
[conda] nvidia-ml-py              13.580.82                pypi_0    pypi
[conda] nvidia-nccl-cu12          2.27.3                   pypi_0    pypi
[conda] nvidia-nvjitlink-cu12     12.8.93                  pypi_0    pypi
[conda] nvidia-nvtx-cu12          12.8.90                  pypi_0    pypi
[conda] pyzmq                     27.1.0                   pypi_0    pypi
[conda] torch                     2.8.0                    pypi_0    pypi
[conda] torchaudio                2.8.0                    pypi_0    pypi
[conda] torchvision               0.23.0                   pypi_0    pypi
[conda] transformers              4.57.0                   pypi_0    pypi
[conda] triton                    3.4.0                    pypi_0    pypi

==============================
         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    GPU4    GPU5    GPU6    GPU7    CPU Affinity    NUMA Affinity   GPU NUMA ID
GPU0     X      PIX     PIX     PIX     SYS     SYS     SYS     SYS     20-39,60-79     1               N/A
GPU1    PIX      X      PIX     PIX     SYS     SYS     SYS     SYS     20-39,60-79     1               N/A
GPU2    PIX     PIX      X      PIX     SYS     SYS     SYS     SYS     20-39,60-79     1               N/A
GPU3    PIX     PIX     PIX      X      SYS     SYS     SYS     SYS     20-39,60-79     1               N/A
GPU4    SYS     SYS     SYS     SYS      X      PIX     PIX     PIX     20-39,60-79     1               N/A
GPU5    SYS     SYS     SYS     SYS     PIX      X      PIX     PIX     20-39,60-79     1               N/A
GPU6    SYS     SYS     SYS     SYS     PIX     PIX      X      PIX     20-39,60-79     1               N/A
GPU7    SYS     SYS     SYS     SYS     PIX     PIX     PIX      X      20-39,60-79     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
==============================
CUDA_HOME=/usr/local/cuda-12.4
CUDA_HOME=/usr/local/cuda-12.4
LD_LIBRARY_PATH=/usr/local/cuda-12.4/lib64:/usr/local/cuda-12.1/lib64:/usr/local/cuda-12.1/lib64:
PYTORCH_NVML_BASED_CUDA_CHECK=1
TORCHINDUCTOR_COMPILE_THREADS=1
CUDA_MODULE_LOADING=LAZY

🐛 Describe the bug

Summary

I’m using Qwen3-VL-8B-Thinking for a video moment retrieval task, and I noticed a significant difference in behavior between vLLM and Transformers inference:

1. When running the same prompt and video(link: https://pan.quark.cn/s/58d7264426b0, ~6mins) input through both frameworks, the time range grounding from the vLLM inference are noticeably less accurate, while Transformers produces more precise temporal localization results.
2. Moreover, when using vLLM, the model is likely output repetitive or meaningless text loops.

The prompt and model output are shown below.
(⚠️ Careful — long text below.)

The correct time range should be

• [17s, 36s] for elderly people doing exercises
• [205s, 260s] for people playing basketball**

Could you please help check whether this is a known issue or provide suggestions on possible causes or fixes?
Happy to share more logs or debugging info if needed — thanks a lot for your help!

Prompt

You are a professional video content analyst and editor. I will provide you with a recorded video file. First, watch the entire video carefully, then identify and extract segments that contain group activities, and output the results in JSON format.

Requirements: If the video contains clear human subjects, make sure not to miss any close-up shots, facial expressions, or actions involving those people.

Note that your output must strictly follow the format example below:
{
    "Segments": [
        {
            "Scene Description": "A woman slowly turns her head and smiles gently at the camera.",
            "Time Range": "from 37.1s to 52.6s"
        },
        {
            "Scene Description": "A group of students having a gathering, smiling happily.",
            "Time Range": "from 80.3s to 90.2s"
        }
    ]
}

Transformers output **(correct in terms of time range grounding)**

So, let's analyze the video. First, I need to find segments with group activities. Let's go through the timestamps.

Looking at the video description:

- Around 25-35 seconds: People doing tai chi or similar group exercise. That's a group activity.
- 68-105 seconds: Playground with kids and adults, but maybe group play. Wait, the playground has colorful surfaces and equipment. But the group activity might be the ones on the blue and purple areas. Wait, the video mentions "a group of people doing a group exercise" in the tai chi part.
- Then, 199-259 seconds: Tennis court with people playing. That's a group activity (two teams playing tennis).
- Also, 20-28 seconds: People walking, but maybe not group. Let's check the key moments.

Wait, the user wants group activities. Let's identify:

1. Tai chi group: from 25s to 35s (approximately). The description says "a group of people doing tai chi or similar group exercise. The scene shows multiple people in colorful clothes performing synchronized movements." So Time Range for that is around 25-35 seconds.

2. Tennis court: 199-259 seconds. The description mentions "a tennis court where two teams of people are engaged in a match. The players are actively moving, hitting the ball back and forth." So that's a group activity.

Are there other group activities?

- Playground: kids might be playing, but is it a group activity? The video shows "a playground with colorful surfaces and equipment" but maybe not a specific group action. The user says "group activities", so if it's multiple people doing something together, like the tai chi and tennis.

Check the time ranges:

Tai chi: the video description says at 25-35 seconds, "a group of people in colorful traditional attire performing synchronized movements". So that's a group activity.

Tennis court: the tennis court is from around 199-259 seconds (the video description says "a tennis court where two teams of people are engaged in a match" and the timestamps for the tennis court part would be when the camera shows the tennis court, which is after the playground. Let's check the timestamps in the video:

The video goes from the plaza to playground (70-100s), then the blue track (130-150s), then the tennis court around 199-259s.

Another possible group activity: the plaza with people walking and scooters, but that's more individual. The tai chi and tennis are the main group activities.

Wait, the video description says:

At 25-35s: "a group of people in colorful traditional attire performing synchronized movements (tai chi or similar group exercise)"

At 199-259s: "a tennis court where two teams of people are engaged in a match"

Also, the playground area: are there groups? The description says "a playground with colorful surfaces and equipment" but maybe kids playing, but it's not specified as a group activity. The key is "group activities" meaning multiple people together doing something.

Let's confirm the time ranges for each:

Tai chi: 25-35 seconds (approx, based on the video description). The video says "at 25-35s, a group of people in colorful traditional attire performing synchronized movements".

Tennis court: the tennis court starts around 198-202 seconds (when the camera approaches the tennis court) and goes to 257-259 seconds (when the camera leaves). So time range 199-259 seconds.

Are there others?

Another part: 27-35s, the group doing tai chi. Also, in the playground area, but maybe not a group activity. The main group activities are the tai chi and the tennis match.

Now, check for close-ups or facial expressions. For the tai chi, the description mentions "colorful traditional attire performing synchronized movements", so it's a group. For the tennis, it's two teams playing, so group.

So the JSON should have these two segments.
</think>

**{
    "Segments": [
        {
            "Scene Description": "A group of people in colorful traditional attire performing synchronized movements, likely tai chi, in an open plaza area.",
            "Time Range": "from 25.1s to 35.3s"
        },
        {
            "Scene Description": "Two teams of people engaged in a tennis match on a blue court, actively hitting the ball back and forth with athletic movements.",
            "Time Range": "from 199.5s to 259.2s"
        }
    ]
}**

vLLM output (incorrect)

So, let's analyze the video. First, I need to find segments with group activities. Let's go through each timestamp:

- At 27.5s: There's a group of people doing morning exercises (Tai Chi or similar). The description says "a group of people in light-colored clothing performing synchronized movements." That's a group activity.

- At 195.3s: A basketball court with people playing basketball. The scene shows "a group of people playing basketball on an outdoor court." That's another group activity.

- At 220.7s: A group of people on a blue running track. The description mentions "a group of people jogging or walking on a blue running track." So that's a group activity.

Wait, let's check the timestamps again. The video segments:

Looking at the provided timestamps:

- 27.5s: "a group of people in light-colored clothing performing synchronized movements" (this is the morning exercise scene).

- 195.3s: "a group of people playing basketball on an outdoor court" (basketball game).

- 220.7s: "a group of people jogging or walking on a blue running track" (running track).

Are there any other group activities? Let's check:

- 50.7s: "a person pushing a stroller" – that's individual.

- 73.2s: "a person on a scooter" – individual.

- 95.7s: Playground with kids, but maybe not a group activity? Wait, the playground scene (95.7s) might have kids playing, but the description says "a playground with colorful equipment" but not a group activity. The group activities are when multiple people are interacting together.

So the key group activities are:

1. Morning exercise (27.5s): group of people doing synchronized movements.

2. Basketball game (195.3s): group playing basketball.

3. Running track (220.7s): group jogging/walking.

Wait, let's check the exact timestamps from the user's input:

The user provided the video segments with timestamps:

<27.5 seconds> ... "a group of people in light-colored clothing performing synchronized movements" – that's the first group activity.

<195.3 seconds> ... "a group of people playing basketball on an outdoor court" – second.

<220.7 seconds> ... "a group of people jogging or walking on a blue running track" – third.

Are there any others?

Check other timestamps:

- 50.7s: "a person pushing a stroller" – individual.

- 73.2s: "a person on a scooter" – individual.

- 95.7s: "a playground with colorful equipment" – maybe kids playing, but the description says "a playground with colorful equipment" but not a group activity. Unless it's a group of kids, but the user's description for that segment is "a playground with colorful equipment" which might not specify group activity.

- 118.3s: "a person walking on a path" – individual.

- 140.8s: "a person riding a scooter" – individual.

- 163.3s: "a person walking on a path" – individual.

- 185.8s: "a person walking on a path" – individual.

- 208.4s: "a group of people playing basketball" – wait, 208.4s is the basketball court scene. Wait, the user's input has:

<208.4 seconds> ... "a group of people playing basketball on an outdoor court" – yes, that's the basketball scene.

Wait, the user's input has:

<208.4 seconds> ... "a group of people playing basketball on an outdoor court" – that's the basketball game.

Then <220.7 seconds> is the running track.

So the group activities are:

1. 27.5s: morning exercise group.

2. 208.4s: basketball game.

3. 220.7s: running track group.

Wait, let's check the exact time ranges. The user's input has:

<27.5 seconds> ... "a group of people in light-colored clothing performing synchronized movements" – so the time range is around 27.5s.

<208.4 seconds> ... "a group of people playing basketball on an outdoor court" – time around 208.4s.

<220.7 seconds> ... "a group of people jogging or walking on a blue running track" – time around 220.7s.

Now, need to make sure these are group activities. The first one is a group doing exercises, the second is a basketball game (group), the third is a group jogging.

Are there any other group activities? Let's check:

- 50.7s: "a person pushing a stroller" – no.

- 73.2s: "a person on a scooter" – no.

- 95.7s: "a playground with colorful equipment" – maybe kids, but the description doesn't say a group activity. It's a playground, but the activity might be individual.

- 118.3s: "a person walking on a path" – no.

- 140.8s: "a person riding a scooter" – no.

- 163.3s: "a person walking on a path" – no.

- 185.8s: "a person walking on a path" – no.

- 208.4s: basketball – yes.

- 220.7s: running track – yes.

- 27.5s: morning exercise – yes.

So three segments.

Now, the scene descriptions need to be precise. Let's write them:

1. For 27.5s: "A group of elderly people in light-colored clothing performing synchronized morning exercises in an open plaza."

2. For 208.4s: "A group of people playing basketball on an outdoor court surrounded by modern buildings."

3. For 220.7s: "A group of people jogging or walking together on a blue running track in a park."

Wait, the user's example has "from Xs to Ys", so the time range should be the start and end of the segment. But the user's input gives the timestamp as the start of the segment. Wait, the user's input is:

<27.5 seconds> ... so the segment starts at 27.5s. But how long does it last? The user's input doesn't specify the end time, but in the example, the time range is "from 37.1s to 52.6s". So we need to estimate the duration. But since the user's input is a list of segments with timestamps, perhaps each timestamp is the start of the segment, and the segment is a short duration. But the problem says "identify and extract segments that contain group activities", so we need to find the time range where the group activity occurs.

Wait, the user's input is a sequence of video frames with timestamps. For example, the first frame is at 0s, then 27.5s, etc. So each timestamp is the start of a new segment. So the group activity at 27.5s is a segment that starts at 27.5s. But how long does it last? The problem says "output the results in JSON format" with "Time Range" as "from Xs to Ys". So we need to determine the start and end time of the group activity.

But since the user's input is a list of segments (each with a timestamp), perhaps each segment is a short clip. For example, the segment at 27.5s is a clip where the group activity is happening. So the time range would be from 27.5s to, say, 35s (assuming the clip is 7.5s long). But the user's example has "from 37.1s to 52.6s", which is a duration of 15.5s.

But the problem states: "If the video contains clear human subjects, make sure not to miss any close-up shots, facial expressions, or actions involving those people."

So for the group activity at 27.5s, the scene is a group of people doing exercises. Let's assume the segment is from 27.5s to 35s (as an example). But how to determine the end time?

Wait, the user's input is a list of video segments with timestamps. Each timestamp is the start of the segment. So for example, the first segment is 0s, then 27.5s, etc. So the segment at 27.5s is a clip that starts at 27.5s. The duration of each clip is not given, but in the example, the time range is from X to Y.

But the problem says "identify and extract segments that contain group activities". So we need to find the time range where the group activity occurs. Since the user's input provides the timestamp as the start of the segment, but the duration is not specified, perhaps we can assume that the segment is a short duration, and the time range is from the start timestamp to the next timestamp minus a small amount.

Alternatively, maybe the user's input is a list of keyframes, and the group activity occurs in the segment between two timestamps.

Wait, the user's input is:

<5.6 seconds> ... (first frame)

<27.5 seconds> ... (second frame)

<50.7 seconds> ... (third frame)

etc.

So each timestamp is the start of a new segment. So the segment from 27.5s to 50.7s is the next segment. But the group activity in the 27.5s segment is the morning exercise.

Wait, the user's input for 27.5s is: "a group of people in light-colored clothing performing synchronized movements". So that's the group activity. So the time range for that segment is from 27.5s to the next timestamp, which is 50.7s. But the next timestamp is 50.7s, which is a different scene (person on scooter). So the group activity at 27.5s is from 27.5s to 50.7s? But that's a long time. Alternatively, the group activity is a short clip within that segment.

But the problem says "output the results in JSON format" with "Time Range" as "from Xs to Ys". So we need to find the start and end time of the group activity.

Given that the user's input is a list of segments with timestamps, and each segment is a short clip, perhaps the group activity occurs in the segment that starts at 27.5s, and the duration is the time between 27.5s and the next segment's start time (50.7s). But that's 23.2s, which is too long. Alternatively, the group activity is a short part within that segment.

But the problem states: "If the video contains clear human subjects, make sure not to miss any close-up shots, facial expressions, or actions involving those people."

So for the morning exercise scene, the group activity is visible in the segment starting at 27.5s. Let's assume that the group activity is present from 27.5s to 35s (for example), but since the user's input doesn't specify the duration, perhaps we can take the start time as the beginning of the segment and the end time as the next segment's start time minus a small buffer.

Alternatively, the user's example has "from 37.1s to 52.6s", which is a duration of 15.5s. So perhaps the time range is the start time to the next timestamp minus a small amount.

But given the problem's requirements, we need to output the time range for each group activity segment.

Let's list all possible group activities:

1. 27.5s: group of people doing exercises. Let's say the segment is from 27.5s to 35s (assuming the clip is 7.5s).

2. 208.4s: basketball game. From 208.4s to 220s.

3. 220.7s: running track group. From 220.7s to 230s.

But how to determine the exact end time? Since the user's input is a list of segments with timestamps, perhaps each segment is a short clip, and the group activity is the main part of that segment.

Alternatively, the user's input for 27.5s is the start of the segment where the group activity occurs, and the segment ends at the next timestamp (50.7s). But that's a long time. However, the problem says "identify and extract segments that contain group activities", so the segment that contains the group activity is the one where the group activity is visible.

Given that the user's input for 27.5s is "a group of people in light-colored clothing performing synchronized movements", that's the group activity. So the time range is from 27.5s to the next timestamp (50.7s), but that's not accurate. Alternatively, the group activity is a short clip within that segment.

But since the problem requires the time range, and the user's example uses a specific range, perhaps we can take the start time as the timestamp provided and the end time as the next timestamp minus a small amount.

Alternatively, the user's input is a list of keyframes, and the group activity occurs in the segment between two keyframes.

Wait, the user's input is:

<27.5 seconds> ... (this is the start of a segment where the group activity is happening)

<50.7 seconds> ... (next segment)

So the group activity is in the segment from 27.5s to 50.7s.

Similarly, for the basketball scene:

<208.4 seconds> ... (start of segment)

<220.7 seconds> ... (next segment)

So the basketball group activity is from 208.4s to 220.7s.

For the running track:

<220.7 seconds> ... (start of segment)

<233.1 seconds> ... (next segment)

So the running track group activity is from 220.7s to 233.1s.

But the user's input for the basketball scene is at 208.4s, and the next segment is 220.7s. So the time range for basketball is 208.4s to 220.7s.

Similarly, the running track is 220.7s to 233.1s.

The morning exercise is 27.5s to 50.7s.

Now, let's check the user's input for each:

- 27.5s: "a group of people in light-colored clothing performing synchronized movements" → group activity.

- 208.4s: "a group of people playing basketball on an outdoor court" → group activity.

- 220.7s: "a group of people jogging or walking on a blue running track" → group activity.

So these are the three group activities.

Now, the scene descriptions:

1. Morning exercise: "A group of elderly people in light-colored clothing performing synchronized morning exercises in an open plaza."

2. Basketball: "A group of people playing basketball on an outdoor court surrounded by modern buildings."

3. Running track: "A group of people jogging or walking together on a blue running track in a park."

Time ranges:

1. 27.5s to 50.7s

2. 208.4s to 220.7s

3. 220.7s to 233.1s

Wait, but the user's input for the running track is at 220.7s, and the next segment is 233.1s (from the user's input: <233.1 seconds> ...). So the time range for the running track is 220.7s to 233.1s.

Now, let's confirm:

- The first group activity is at 27.5s, which is the segment where people are doing exercises. The next segment is 50.7s, so the time range is 27.5s to 50.7s.

- The basketball scene is at 208.4s, next segment 220.7s → 208.4s to 220.7s.

- The running track is at 220.7s, next segment 233.1s → 220.7s to 233.1s.

Yes.

Now, check if there are any other group activities.

- 50.7s: "a person pushing a stroller" → individual.

- 73.2s: "a person on a scooter" → individual.

- 95.7s: "a playground with colorful equipment" → maybe kids, but the description doesn't specify a group activity. It's a playground, but the activity might be individual.

- 118.3s: "a person walking on a path" → individual.

- 140.8s: "a person riding a scooter" → individual.

- 163.3s: "a person walking on a path" → individual.

- 185.8s: "a person walking on a path" → individual.

- 208.4s: basketball → group.

- 220.7s: running track → group.

- 27.5s: morning exercise → group.

So only three group activities.

Now, write the JSON:

{
    "Segments": [
        {
            "Scene Description": "A group of elderly people in light-colored clothing performing synchronized morning exercises in an open plaza.",
            "Time Range": "from 27.5s to 50.7s"
        },
        {
            "Scene Description": "A group of people playing basketball on an outdoor court surrounded by modern buildings.",
            "Time Range": "from 208.4s to 220.7s"
        },
        {
            "Scene Description": "A group of people jogging or walking together on a blue running track in a park.",
            "Time Range": "from 220.7s to 233.1s"
        }
    ]
}
</think>

**{
    "Segments": [
        {
            "Scene Description": "A group of elderly people in light-colored clothing performing synchronized morning exercises in an open plaza.",
            "Time Range": "from 27.5s to 50.7s"
        },
        {
            "Scene Description": "A group of people playing basketball on an outdoor court surrounded by modern buildings.",
            "Time Range": "from 208.4s to 220.7s"
        },
        {
            "Scene Description": "A group of people jogging or walking together on a blue running track in a park.",
            "Time Range": "from 220.7s to 233.1s"
        }
    ]
}**

vLLM output (meaningless loop)

So, let's analyze the video. First, I need to find segments with group activities. Let's go through each timestamp:

- At 27.5s: There's a group of people doing morning exercises (Tai Chi or similar). The description says "a group of people in light-colored clothing performing synchronized movements." That's a group activity.

- At 195.2s: A basketball court with people playing basketball. The scene shows "a group of people playing basketball on an outdoor court." That's another group activity.

- At 220.7s: A group of people on a blue path, maybe walking or jogging. The description mentions "a group of people walking or jogging on a blue path." Wait, but let's check the video. The 195.2s is basketball, 220.7s might be another group. Wait, the timestamps:

Looking at the provided frames:

- 27.5s: Group doing exercises (Tai Chi). That's a clear group activity.

- 195.2s: Basketball court with people playing. Group activity.

- 220.7s: Maybe a group on the path? Wait, the 195.2s is basketball, then 220.7s is another scene. Let's check the video description:

The video segments:

- 27.5s: "a group of people in light-colored clothing performing synchronized movements" (Tai Chi).

- 195.2s: "a group of people playing basketball on an outdoor court."

- 220.7s: "a group of people walking or jogging on a blue path." Wait, but the user's example has specific scenes. Let's check the timestamps given in the problem.

Wait, the user provided the video with timestamps like [5.6s], [28.2s], etc. Wait, the problem says "I will provide you with a recorded video file. First, watch the entire video carefully, then identify and extract segments that contain group activities..."

The user's input has the video segments with timestamps. Let's list the key scenes:

- 28.2s: "a group of people in light-colored clothing performing synchronized movements" (Tai Chi). That's a group activity.

- 195.2s: "a group of people playing basketball on an outdoor court."

- 220.7s: "a group of people walking or jogging on a blue path." Wait, but let's check the actual video description.

Wait, the user's input has:

<28.2 seconds> ... "a group of people in light-colored clothing performing synchronized movements" (Tai Chi).

<195.2 seconds> ... "a group of people playing basketball on an outdoor court."

<220.7 seconds> ... "a group of people walking or jogging on a blue path."

Wait, but the problem says "group activities". Let's confirm each:

1. 28.2s: Group doing Tai Chi. That's a group activity. Time range: 28.2s to 35.5s (since the next scene is 50.7s, but the description says "a group of people in light-colored clothing performing synchronized movements" at 28.2s. So the segment is from 28.2s to 35.5s.

2. 195.2s: Basketball court. The scene is "a group of people playing basketball on an outdoor court." The time range would be 195.2s to 220.7s (since the next scene is 220.7s).

3. 220.7s: Group walking/jogging. The description says "a group of people walking or jogging on a blue path." Time range 220.7s to 235.9s.

Wait, but let's check the exact timestamps from the user's input:

The user's input has:

<28.2 seconds> ... (scene with Tai Chi)

<50.7 seconds> ... (next scene)

<195.2 seconds> ... (basketball)

<220.7 seconds> ... (group on path)

<235.9 seconds> ... (next scene)

So the group activities are:

- Tai Chi group: 28.2s to 35.5s (assuming the scene lasts until 35.5s, but the user's input shows the next scene at 50.7s. Wait, the user's input has the video segments as:

[5.6s] ... [28.2s] ... [50.7s] ... [73.2s] ... [95.7s] ... [118.3s] ... [140.8s] ... [163.3s] ... [185.8s] ... [208.4s] ... [230.9s] ... [253.4s] ... [276.0s] ... [298.5s] ... [321.0s] ... [343.5s]

Wait, the user's input is a list of timestamps with descriptions. Let's parse the user's input:

The user provided:

<5.6 seconds> ... (scene 1)

<28.2 seconds> ... (scene 2: group doing Tai Chi)

<50.7 seconds> ... (scene 3)

<73.2 seconds> ... (scene 4)

<95.7 seconds> ... (scene 5: playground)

<118.3 seconds> ... (scene 6)

<140.8 seconds> ... (scene 7)

<163.3 seconds> ... (scene 8)

<185.8 seconds> ... (scene 9)

<208.4 seconds> ... (scene 10: basketball court)

<230.9 seconds> ... (scene 11)

<253.4 seconds> ... (scene 12)

<276.0 seconds> ... (scene 13)

<298.5 seconds> ... (scene 14)

<321.0 seconds> ... (scene 15)

<343.5 seconds> ... (scene 16)

So the group activities are:

- Scene 2 (28.2s): group doing Tai Chi. The description says "a group of people in light-colored clothing performing synchronized movements". So this is a group activity.

- Scene 10 (208.4s): basketball court. The description says "a group of people playing basketball on an outdoor court". So this is a group activity.

- Scene 11 (230.9s): "a group of people playing basketball on an outdoor court" (wait, no, scene 10 is 208.4s, scene 11 is 230.9s. Let's check the user's input:

The user's input for scene 10: <208.4 seconds> ... "a group of people playing basketball on an outdoor court."

Scene 11: <230.9 seconds> ... "a group of people playing basketball on an outdoor court" (wait, no, the user's input for scene 11 is "a group of people playing basketball on an outdoor court" but maybe it's the same as scene 10? Wait, no, the user's input has:

<208.4 seconds> ... "a group of people playing basketball on an outdoor court."

<230.9 seconds> ... "a group of people playing basketball on an outdoor court" (but maybe it's a different angle or same activity).

Wait, the user's input for scene 10: "a group of people playing basketball on an outdoor court."

Scene 11: "a group of people playing basketball on an outdoor court" (but maybe it's the same activity, so the time range would be 208.4s to 230.9s.

Another group activity: scene 2 (28.2s) is Tai Chi group.

Are there any other group activities?

Scene 14: <298.5 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 14 is "a group of people walking or jogging on a blue path." Let's check:

The user's input:

<298.5 seconds> ... "a group of people walking or jogging on a blue path."

Yes, that's another group activity.

Wait, let's list all scenes with group activities:

1. 28.2s: Tai Chi group. Time range: 28.2s to 35.5s (assuming the scene lasts until the next scene at 50.7s, but the user's input shows the next scene at 50.7s, so the duration is 50.7 - 28.2 = 22.5s. But the problem says "Time Range" as "from Xs to Ys". So for scene 2, it's from 28.2s to 50.7s? Wait, no. The user's input has the scenes as separate timestamps. For example, <28.2 seconds> is the start of the scene, and the next scene is <50.7 seconds>, so the duration of the Tai Chi scene is 50.7 - 28.2 = 22.5s. So the time range is 28.2s to 50.7s.

Wait, but the problem says "Time Range" as "from 37.1s to 52.6s" in the example. So the start time is the timestamp of the scene, and the end time is the next scene's timestamp.

So for the Tai Chi scene (28.2s), the next scene is 50.7s, so time range is 28.2s to 50.7s.

For the basketball scene (208.4s), the next scene is 230.9s, so time range 208.4s to 230.9s.

For the group walking/jogging scene (298.5s), the next scene is 321.0s, so time range 298.5s to 321.0s.

Wait, let's check the user's input:

<28.2 seconds> ... (Tai Chi group)

<50.7 seconds> ... (next scene)

<208.4 seconds> ... (basketball group)

<230.9 seconds> ... (next scene)

<298.5 seconds> ... (group walking/jogging)

<321.0 seconds> ... (next scene)

So the group activities are:

1. Tai Chi group: 28.2s to 50.7s.

2. Basketball group: 208.4s to 230.9s.

3. Group walking/jogging: 298.5s to 321.0s.

Now, check if there are any close-up shots or facial expressions. The problem says "If the video contains clear human subjects, make sure not to miss any close-up shots, facial expressions, or actions involving those people."

For the Tai Chi scene: "a group of people in light-colored clothing performing synchronized movements" – this is a group activity, but does it have close-ups? The user's input description doesn't mention close-ups, but the problem says to check. However, the user's input description for scene 2 is "a group of people in light-colored clothing performing synchronized movements", so it's a group activity.

For the basketball scene: "a group of people playing basketball on an outdoor court" – group activity.

For the walking/jogging scene: "a group of people walking or jogging on a blue path" – group activity.

Now, let's confirm each:

- Tai Chi group: 28.2s to 50.7s. Scene description: "A group of people in light-colored clothing performing synchronized movements (Tai Chi)."

- Basketball group: 208.4s to 230.9s. Scene description: "A group of people playing basketball on an outdoor court."

- Walking/jogging group: 298.5s to 321.0s. Scene description: "A group of people walking or jogging on a blue path."

Wait, but the user's input for scene 14 is <298.5 seconds> ... "a group of people walking or jogging on a blue path." So that's correct.

Now, check if there are any other group activities. Let's see:

Scene 5: <95.7 seconds> ... "a playground with colorful slides and equipment" – no group activity, just the playground.

Scene 7: <140.8 seconds> ... "a group of people walking or jogging on a blue path" – wait, no, scene 7 is <140.8 seconds> ... "a group of people walking or jogging on a blue path" – but the user's input says:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

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<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

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<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

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<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input has:

<140.8 seconds> ... "a group of people walking or jogging on a blue path." Wait, no, the user's input for scene 7 is:

<140.8 seconds> ... "a group of people walking or jogging on a

Reproduce

Below are the vLLM setting and inference code of vLLM and transformers.

vllm serve Qwen/Qwen3-VL-8B-Thinking     \
--tensor-parallel-size 8    \
--allowed-local-media-path /     \
--mm-encoder-tp-mode data    \
--async-scheduling    \
--host 0.0.0.0    \
--port 30000

Qwen3-VL inference code using Transformers
Qwen3-VL inference code using 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.

[0xrushi]

Strange, I tried FP8 version, single gpu, I didn't face the issue vllm version 0.11.1rc2.dev59+g938c43ea7.cu129

[Steven0236]

Can you try testing using different backends (flashinfer / flash_attn) to check whether there's something wrong in the attention backend?

There are already two reported cases of lost precision using 0.11.1. Check #26930 and #26936. In my case the loss of precision happens only when using flashinfer backend.

Interestingly all three reported loss of precision happen in Qwen models, and all with different architecture (MoE, MoE with linear attention, and vision). However, I tried the simple dense model Qwen3-4b-2507 and that one seemed to work normally.

[Xqle]

Strange, I tried FP8 version, single gpu, I didn't face the issue vllm version 0.11.1rc2.dev59+g938c43ea7.cu129

@0xrushi
It seems that the issue might be related to the vLLM version.
Currently, the latest official release I can find is v0.11.0.
May I ask how you installed v0.11.1?
I’ll try upgrading to the same build to check if the issue has already been fixed.

[0xrushi]

@Xqle I don't remember, try

uv pip install -U vllm \
    --torch-backend=auto \
    --extra-index-url https://wheels.vllm.ai/nightly

or

pip install -U vllm \
    --pre \
    --extra-index-url https://wheels.vllm.ai/nightly

[Xqle]

@StevenShi-23 I have also tested the dense model Qwen3-VL-8B-Thinking, but the the performance when using vLLM remains consistently less accurate than using transformers.
By the way, could you please share how to switch between different attention backends. Should I set an environment variable or pass a specific parameter when launching vLLM?

[Steven0236]

You should first try turning off only the flashinfer sampler. It's been reported to be defective for some models. That is done by setting the environment variable:

VLLM_USE_FLASHINFER_SAMPLER="0"

If that doesn't solve the problem, then you should try changing the attention backend by setting the environment variable.:

VLLM_ATTENTION_BACKEND="FLASH_ATTN"

You should also test running a dense text-only model like Qwen3-4b-2507-instruct, because that one seems to work fine on my system (although my test is rather superficial).

Look forward to seeing your results with the different combinations.

[Steven0236]

@Xqle
The problem, perhaps coincidentally, appeared when vllm switched to using flashinfer 0.4.x.
I saw it on 0.11.1rc2.dev47 and even earlier on 0.11.0rc2.dev408 (all built from source).
Because vllm is somewhat locked to the flashinfer version, it's hard to troubleshoot whether the problem is inside the vllm code or that of flashinfer.

[Steven0236]

You can check the flashinfer bug report for more info: flashinfer-ai/flashinfer#1931

[xjtupanda]

Perhaps the generation parameters do not fully align.

1. This might be the primary cause. The fps is set to 1 for transformers in your code (default fps is 2), and it is said that the online serving version of the vLLM inference code only samples 32 frames. Seems like the fps setting problem is not well-addressed yet. (How to pass in fps with VLLM? QwenLM/Qwen3-VL#1517 & vllm和transformers结果不一致 QwenLM/Qwen3-VL#1557)
2. The Qwen3-VL usage guide mentions that:

Specifying --async-scheduling improves the overall system performance by overlapping scheduling overhead with the decoding process. However, this experimenal feature currently does not support speculative decoding, sampling with penalties, or other scenarios where decoding outcome depends on the output from a previous step.

So the repetition penalty may not take effect. Try remove the --async-scheduling when launching.

3. The 'top_k' param seems to be missing in the sampling params for vLLM, and the default values for params such as 'temperature' seem inconsistent with the official default

[Steven0236]

I've just tested Qwen3-VL-8B-Instruct-FP8 with the configuration below, and it does not seem to suffer from precision loss in text processing. In fact when testing it with Cline (which is quite demanding on tool calling), it's surprisingly good on agentic coding for a vision model. So I suspect the problem you were seeing had something to do with image processing and not with the accuracy of the model inferencing.

- Environment Variables
VLLM_ATTENTION_BACKEND="FLASHINFER"
VLLM_USE_FLASHINFER_SAMPLER="0"

vllm serve /AI2/vllm_models/Vision/2510/Qwen3-VL-8B-Instruct-FP8 \
  --trust-remote-code                                         \
  --max-model-len                  128000                     \
  --max-num-seqs                   1                          \
  --enable-chunked-prefill                                    \
  --tensor-parallel-size           4                          \
  --pipeline-parallel-size         1                          \
  --kv-cache-dtype                 fp8                        \
  --gpu-memory-utilization         0.78                     

Cline Input: Read the file Oclient.py and let us discuss it.

Cline Output: The user has requested to read the file Oclient.py and discuss it. Since I am in PLAN MODE, I should first read the file to understand its content before discussing it. This will allow me to provide a detailed and accurate discussion. I will use the read_file tool to read the contents of Oclient.py.

Cline wants to read this file: Oclient.py‎
I have successfully read the contents of Oclient.py. The file contains a class Oclient with various methods for managing and testing models, interacting with an Oserver, and monitoring server performance. The class includes methods for starting and stopping models, monitoring server status, listing models, running tests, and displaying metrics.
...

[Xqle]

@xjtupanda
Thanks, I tried to modify my code to align with transformers configuration, but I ran into the following error:

Traceback (most recent call last):
  File "/data01/xuqile/code/Inference/vllm/infer_qwen3vl_vllm_online.py", line 227, in <module>
    main()
  File "/data01/xuqile/code/Inference/vllm/infer_qwen3vl_vllm_online.py", line 206, in main
    response = client.chat.completions.create(
  File "/data01/xuqile/miniforge3/envs/qwen3vl-vllm/lib/python3.10/site-packages/openai/_utils/_utils.py", line 286, in wrapper
    return func(*args, **kwargs)
TypeError: Completions.create() got an unexpected keyword argument 'top_k'

Here is my current inference code:

generation_config = GenerationConfig.from_pretrained(args.model_path) 
print("=====Generation Config=====")
print(generation_config)
start_time = time.time()
response = client.chat.completions.create(
    model=args.model_path,
    messages=messages,
    max_tokens=args.max_tokens,
    temperature=generation_config.temperature,
    top_p=generation_config.top_p,
    top_k=generation_config.top_k,
    extra_body={
        "skip_special_tokens": False,
        "repetition_penalty": generation_config.repetition_penalty,
    },
)
end_time = time.time()
elapsed_time = end_time - start_time

Any tips on how to correctly pass the top_k argument? Should I simply add it to the extra_body?
Also, is there a way to verify whether my configuration is correctly applied during inference?

Looking forward to your reply!

[Xqle]

@Steven0236
Thanks, I also think the issue might be related to the video preprocessing.
As @xjtupanda mentioned, vLLM online serving only samples 32 frames when inferencing, which doesn’t seem ideal.
It also seems that there is currently no way for us to specify the fps argument.