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Qwen2.5-VL-7B-Instruct-quantized.w8a8

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metadata
tags:
  - vllm
  - vision
  - w8a8
license: apache-2.0
license_link: >-
  https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
language:
  - en
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers

Qwen2.5-VL-7B-Instruct-quantized-w8a8

Model Overview

  • Model Architecture: Qwen/Qwen2.5-VL-7B-Instruct
    • Input: Vision-Text
    • Output: Text
  • Model Optimizations:
    • Weight quantization: INT8
    • Activation quantization: INT8
  • Release Date: 2/24/2025
  • Version: 1.0
  • Model Developers: Neural Magic

Quantized version of Qwen/Qwen2.5-VL-7B-Instruct.

Model Optimizations

This model was obtained by quantizing the weights of Qwen/Qwen2.5-VL-7B-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/Qwen2.5-VL-7B-Instruct-quantized.w8a8",
    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 (
    TraceableQwen2_5_VLForConditionalGeneration,
)

# Load model.
model_id = "Qwen/Qwen2.5-VL-7B-Instruct"
model = TraceableQwen2_5_VLForConditionalGeneration.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)

# Define a oneshot data collator for multimodal inputs.
def data_collator(batch):
    assert len(batch) == 1
    return {key: torch.tensor(value) for key, value in batch[0].items()}


# Recipe
recipe = [
    GPTQModifier(
        targets="Linear",
        scheme="W8A8",
        sequential_targets=["Qwen2_5_VLDecoderLayer"],
        ignore=["lm_head", "re:visual.*"],
    ),
]

SAVE_DIR==f"{model_id.split('/')[1]}-quantized.w8a8"

# 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=data_collator,
    output_dir=SAVE_DIR
)

Evaluation

The model was evaluated on OpenLLM Leaderboard V1, OpenLLM Leaderboard V2 and on HumanEval, using the following commands:

Evaluation Commands

Accuracy

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/Qwen2.5-VL-7B-Instruct-quantized.w8a8 --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 Model Average Cost Reduction Latency (s) QPD Latency (s)th> QPD Latency (s) QPD
A100x1 Qwen/Qwen2.5-VL-7B-Instruct 2.8 707 1.7 1162 1.7 1198
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a8 1.24 2.4 851 1.4 1454 1.3 1512
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16 1.49 2.2 912 1.1 1791 1.0 1950
H100x1 Qwen/Qwen2.5-VL-7B-Instruct 2.0 557 1.2 919 1.2 941
neuralmagic/Qwen2.5-VL-7B-Instruct-FP8-Dynamic 1.28 1.6 698 0.9 1181 0.9 1219
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16 1.28 1.6 686 0.9 1191 0.9 1228

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
A100x1 Qwen/Qwen2.5-VL-7B-Instruct-quantized. 0.7 1347 2.6 5221 3.0 6122
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a8 1.27 0.8 1639 3.4 6851 3.9 7918
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16 1.21 0.7 1314 3.0 5983 4.6 9206
H100x1 Qwen/Qwen2.5-VL-7B-Instruct 0.9 969 3.1 3358 3.3 3615
neuralmagic/Qwen2.5-VL-7B-Instruct-FP8-Dynamic 1.29 1.2 1331 3.8 4109 4.2 4598
neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16 1.28 1.2 1298 3.8 4190 4.2 4573