Repository: EvolvingLMMs-Lab/lmms-eval
Issue: MMStar for Qwen3-VL-4B cannot reach the official report
Root cause: Interface Contract Mismatch

<img width="1124" height="207" alt="Image" src="https://github.com/user-attachments/assets/c0909cd1-e214-476f-871e-3df45544c7cb" />

While the official report is 69.8!

<img width="417" height="272" alt="Image" src="https://github.com/user-attachments/assets/9b5c267b-86cd-44da-9262-c880fe608ff9" />

Just use the example, and changed `MODEL`, `BATCH_SIZE` and `TENSOR_PARALLEL_SIZE `

```bash
#!/bin/bash

# Qwen3-VL Evaluation Script with vLLM Backend
# This script demonstrates how to evaluate Qwen3-VL models using vLLM for accelerated inference
#
# Requirements:
# - vllm>=0.11.0
# - qwen-vl-utils
# - CUDA-enabled GPU(s)
#
# Installation:
# uv add vllm qwen-vl-utils
# OR
# pip install vllm>=0.11.0 qwen-vl-utils

# ============================================================================
# Configuration
# ============================================================================

# Model Configuration
# Available Qwen3-VL models:
# - Qwen/Qwen3-VL-30B-A3B-Instruct
# - Qwen/Qwen3-VL-30B-A3B-Thinking
# - Qwen/Qwen3-VL-235B-A22B-Instruct
# - Qwen/Qwen3-VL-235B-A22B-Thinking
# MODEL="/home/zm/LLaMA-Factory/saves/qwen3vl-4b/merged/Qwen3-4B-SFT-1"
MODEL="Qwen/Qwen3-VL-4B-Instruct"

# Parallelization Settings
# Adjust based on your GPU configuration
TENSOR_PARALLEL_SIZE=1  # Number of GPUs for tensor parallelism
DATA_PARALLEL_SIZE=1     # Number of GPUs for data parallelism

# Memory and Performance Settings
GPU_MEMORY_UTILIZATION=0.85  # Fraction of GPU memory to use (0.0 - 1.0)
BATCH_SIZE=512                # Batch size for evaluation

# Task Configuration
# Common tasks: mmmu_val, mme, mathvista, ai2d, etc.
# mmmu_val,mme,mmbench,seedbench,mmstar
TASKS="mmstar"

# Output Configuration
OUTPUT_PATH="./logs/qwen3vl_vllm"
LOG_SAMPLES=true
LOG_SUFFIX="qwen3vl_vllm"

# Evaluation Limits (optional)
# LIMIT=100  # Uncomment to limit number of samples (for testing)

# ============================================================================
# NCCL Configuration (for multi-GPU setups)
# ============================================================================
export NCCL_BLOCKING_WAIT=1
export NCCL_TIMEOUT=18000000
# export NCCL_DEBUG=INFO  # Uncomment for debugging

# ============================================================================
# Run Evaluation
# ============================================================================

echo "=========================================="
echo "Qwen3-VL Evaluation with vLLM"
echo "=========================================="
echo "Model: $MODEL"
echo "Tensor Parallel Size: $TENSOR_PARALLEL_SIZE"
echo "Data Parallel Size: $DATA_PARALLEL_SIZE"
echo "Tasks: $TASKS"
echo "Batch Size: $BATCH_SIZE"
echo "Output Path: $OUTPUT_PATH"
echo "=========================================="

# Build the command
CMD="uv run python -m lmms_eval \
    --model vllm \
    --model_args model=${MODEL},tensor_parallel_size=${TENSOR_PARALLEL_SIZE},data_parallel_size=${DATA_PARALLEL_SIZE},gpu_memory_utilization=${GPU_MEMORY_UTILIZATION} \
    --tasks ${TASKS} \
    --batch_size ${BATCH_SIZE} \
    --output_path ${OUTPUT_PATH}"

# Add optional arguments
if [ "$LOG_SAMPLES" = true ]; then
    CMD="$CMD --log_samples --log_samples_suffix ${LOG_SUFFIX}"
fi

if [ ! -z "$LIMIT" ]; then
    CMD="$CMD --limit ${LIMIT}"
fi

# Execute
echo "Running command:"
echo "$CMD"
echo ""

eval $CMD

echo ""
echo "=========================================="
echo "Evaluation Complete!"
echo "Results saved to: $OUTPUT_PATH"
echo "=========================================="

```

I’m facing the same issue with Qwen3-VL-8B as well — any suggestions would be greatly appreciated!

MMMU_VAL is the similar case like above case

Same Issue

Far below the score reported by official

```
vllm serve "Qwen/Qwen3-VL-8B-Instruct" --port 8000 --host 0.0.0.0 --dtype bfloat16 --limit-mm-per-prompt '{"image": 6}' --tensor_parallel_size 8
```

```Bash
export OPENAI_API_KEY="EMPTY"
export OPENAI_API_BASE="http://localhost:8000/v1"
export OPENAI_API_VERSION="Qwen/Qwen3-VL-8B-Instruct"
export TASK=mmstar
python3 -m lmms_eval \
    --model async_openai \
    --model_args model_version=$OPENAI_API_VERSION \
    --tasks $TASK \
    --batch_size 40 \
    --output_path "./results"
```

Official results were reported using VLMEvalKit, which constructs prompts differently from lmms-eval. I was able to closely reproduce the reported scores after replicating VLMEvalKit’s prompt generation method (see #901).