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Qwen2-VL-72B-Instruct-quantized.w4a16

Image-Text-to-Text
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qwen2_vl
vllm
vision
w4a16
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text-generation-inference
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Qwen2-VL-72B-Instruct-quantized-w4a16

Model Overview

  • Model Architecture: Qwen/Qwen2-VL-72B-Instruct
    • Input: Vision-Text
    • Output: Text
  • Model Optimizations:
    • Weight quantization: INT4
    • Activation quantization: FP16
  • Release Date: 2/24/2025
  • Version: 1.0
  • Model Developers: Neural Magic

Quantized version of Qwen/Qwen2-VL-72B-Instruct.

Model Optimizations

This model was obtained by quantizing the weights of Qwen/Qwen2-VL-72B-Instruct to INT8 data type, ready for inference with vLLM >= 0.5.2.

Deployment

Use with vLLM

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from vllm.assets.image import ImageAsset
from vllm import LLM, SamplingParams

# prepare model
llm = LLM(
    model="neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16",
    trust_remote_code=True,
    max_model_len=4096,
    max_num_seqs=2,
)

# prepare inputs
question = "What is the content of this image?"
inputs = {
    "prompt": f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n",
    "multi_modal_data": {
        "image": ImageAsset("cherry_blossom").pil_image.convert("RGB")
    },
}

# generate response
print("========== SAMPLE GENERATION ==============")
outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
print(f"PROMPT  : {outputs[0].prompt}")
print(f"RESPONSE: {outputs[0].outputs[0].text}")
print("==========================================")

vLLM also supports OpenAI-compatible serving. See the documentation for more details.

Creation

This model was created with llm-compressor by running the code snippet below as part a multimodal announcement blog.

Model Creation Code 
import base64
from io import BytesIO
import torch
from datasets import load_dataset
from qwen_vl_utils import process_vision_info
from transformers import AutoProcessor
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.tracing import TraceableQwen2VLForConditionalGeneration
from llmcompressor.transformers.utils.data_collator import qwen2_vl_data_collator
from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy, ActivationOrdering, QuantizationScheme

# Load model.
model_id = "Qwen/Qwen2-VL-72B-Instruct"

model = TraceableQwen2VLForConditionalGeneration.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)

# Oneshot arguments
DATASET_ID = "lmms-lab/flickr30k"
DATASET_SPLIT = {"calibration": "test[:512]"}
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048

# Load dataset and preprocess.
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42)
dampening_frac=0.01

# Apply chat template and tokenize inputs.
def preprocess_and_tokenize(example):
    # preprocess
    buffered = BytesIO()
    example["image"].save(buffered, format="PNG")
    encoded_image = base64.b64encode(buffered.getvalue())
    encoded_image_text = encoded_image.decode("utf-8")
    base64_qwen = f"data:image;base64,{encoded_image_text}"
    messages = [
        {
            "role": "user",
            "content": [
                {"type": "image", "image": base64_qwen},
                {"type": "text", "text": "What does the image show?"},
            ],
        }
    ]
    text = processor.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    image_inputs, video_inputs = process_vision_info(messages)

    # tokenize
    return processor(
        text=[text],
        images=image_inputs,
        videos=video_inputs,
        padding=False,
        max_length=MAX_SEQUENCE_LENGTH,
        truncation=True,
    )

ds = ds.map(preprocess_and_tokenize, remove_columns=ds["calibration"].column_names)

# Recipe
recipe = GPTQModifier(
    targets="Linear",
    config_groups={
        "config_group": QuantizationScheme(
            targets=["Linear"],
            weights=QuantizationArgs(
                num_bits=4,
                type=QuantizationType.INT,
                strategy=QuantizationStrategy.GROUP,
                group_size=128,
                symmetric=True,
                dynamic=False,
                actorder=ActivationOrdering.WEIGHT,
            ),
        ),
    },
    sequential_targets=["Qwen2VLDecoderLayer"],
    ignore=["lm_head", "re:visual.*"],
    update_size=NUM_CALIBRATION_SAMPLES,
    dampening_frac=dampening_frac
)

SAVE_DIR=f"{model_id.split('/')[1]}-quantized.w4a16

# Perform oneshot
oneshot(
    model=model,
    tokenizer=model_id,
    dataset=ds,
    recipe=recipe,
    max_seq_length=MAX_SEQUENCE_LENGTH,
    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
    trust_remote_code_model=True,
    data_collator=qwen2_vl_data_collator,
    output_dir=SAVE_DIR
)

Evaluation

The model was evaluated using mistral-evals for vision-related tasks and using lm_evaluation_harness for select text-based benchmarks. The evaluations were conducted using the following commands:

Evaluation Commands

Vision Tasks

  • vqav2
  • docvqa
  • mathvista
  • mmmu
  • chartqa
vllm serve neuralmagic/pixtral-12b-quantized.w8a8 --tensor_parallel_size 1 --max_model_len 25000 --trust_remote_code --max_num_seqs 8 --gpu_memory_utilization 0.9 --dtype float16 --limit_mm_per_prompt image=7

python -m eval.run eval_vllm \
        --model_name neuralmagic/pixtral-12b-quantized.w8a8 \
        --url http://0.0.0.0:8000 \
        --output_dir ~/tmp \
        --eval_name <vision_task_name>

Text-based Tasks

MMLU 

lm_eval \
  --model vllm \
  --model_args pretrained="<model_name>",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=<n>,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
  --tasks mmlu \
  --num_fewshot 5 \
  --batch_size auto \
  --output_path output_dir

MGSM 

lm_eval \
  --model vllm \
  --model_args pretrained="<model_name>",dtype=auto,max_model_len=4096,max_gen_toks=2048,max_num_seqs=128,tensor_parallel_size=<n>,gpu_memory_utilization=0.9 \
  --tasks mgsm_cot_native \
  --num_fewshot 0 \
  --batch_size auto \
  --output_path output_dir

Accuracy

Category Metric Qwen/Qwen2-VL-72B-Instruct nm-testing/Qwen2-VL-72B-Instruct-quantized.W4A16 Recovery (%)
Vision MMMU (val, CoT)
explicit_prompt_relaxed_correctness		 62.11 60.11 96.78%
VQAv2 (val)
vqa_match		 82.51 82.38 99.84%
DocVQA (val)
anls		 95.01 94.94 99.93%
ChartQA (test, CoT)
anywhere_in_answer_relaxed_correctness		 83.40 80.72 96.78%
Mathvista (testmini, CoT)
explicit_prompt_relaxed_correctness		 66.57 64.66 97.13%
Average Score 77.92 76.56 98.26
Text MGSM (CoT) 68.60 66.45 96.87%
MMLU (5-shot) 82.70 82.35 99.58%

Inference Performance

This model achieves up to xxx speedup in single-stream deployment and up to xxx speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario. The following performance benchmarks were conducted with vLLM version 0.7.2, and GuideLLM.

Benchmarking Command ``` guidellm --model neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16 --target "http://localhost:8000/v1" --data-type emulated --data prompt_tokens=,generated_tokens=,images=,width=,height= --max seconds 120 --backend aiohttp_server ```

Single-stream performance (measured with vLLM version 0.7.2)

Document Visual Question Answering
1680W x 2240H
64/128		 Visual Reasoning
640W x 480H
128/128		 Image Captioning
480W x 360H
0/128
Hardware Number of GPUs Model Average Cost Reduction Latency (s) QPD Latency (s)th> QPD Latency (s) QPD
A100 4 Qwen/Qwen2-VL-72B-Instruct 6.5 77 4.6 110 4.4 113
2 neuralmagic/Qwen2-VL-72B-Instruct-quantized.w8a8 1.85 7.2 139 4.9 206 4.8 211
1 neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16 3.32 10.0 202 5.0 398 4.8 419
H100 4 Qwen/Qwen2-VL-72B-Instruct 4.4 66 3.0 97 2.9 99
2 neuralmagic/Qwen2-VL-72B-Instruct-FP8-Dynamic 1.79 4.7 119 3.3 173 3.2 177
1 neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16 2.60 6.4 172 4.3 253 4.2 259

**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens

**QPD: Queries per dollar, based on on-demand cost at Lambda Labs (observed on 2/18/2025).

Multi-stream asynchronous performance (measured with vLLM version 0.7.2)

Document Visual Question Answering
1680W x 2240H
64/128		 Visual Reasoning
640W x 480H
128/128		 Image Captioning
480W x 360H
0/128
Hardware Model Average Cost Reduction Maximum throughput (QPS) QPD Maximum throughput (QPS) QPD Maximum throughput (QPS) QPD
A100x4 Qwen/Qwen2-VL-72B-Instruct 0.3 169 1.1 538 1.2 595
neuralmagic/Qwen2-VL-72B-Instruct-quantized.w8a8 1.84 1.2 586 4.0 2042 4.6 2270
neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16 2.73 2.4 1256 12.8 6364 16.0 8076
H100x4 Qwen/Qwen2-VL-72B-Instruct 0.5 137 1.2 356 1.3 377
neuralmagic/Qwen2-VL-72B-Instruct-FP8-Dynamic 1.70 1.6 457 4.4 1207 4.8 1296
neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16 2.35 5.2 1400 13.2 3640 14.4 3976

**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens

**QPS: Queries per second.

**QPD: Queries per dollar, based on on-demand cost at Lambda Labs (observed on 2/18/2025).

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