Qwen2-VL-72B-Instruct-quantized.w8a8

Qwen2-VL-72B-Instruct-quantized-w8a8

Model Overview

• Model Architecture: Qwen/Qwen2-VL-72B-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-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.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.

<details> <summary>Model Creation Code</summary>

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


# 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",
        scheme="W8A8",
        sequential_targets=["Qwen2VLDecoderLayer"],
        ignore=["lm_head", "re:visual.*"],
    ),
]

# 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
)

</details>

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:

<details> <summary>Evaluation Commands</summary>

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 \
  --apply_chat_template \
  --num_fewshot 0 \
  --batch_size auto \
  --output_path output_dir

</details>

Accuracy

<table> <thead> <tr> <th>Category</th> <th>Metric</th> <th>Qwen/Qwen2-VL-72B-Instruct</th> <th>neuralmagic/Qwen2-VL-72B-Instruct-quantized.w8a8</th> <th>Recovery (%)</th> </tr> </thead> <tbody> <tr> <td rowspan="6"><b>Vision</b></td> <td>MMMU (val, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td> <td>62.11</td> <td>61.78</td> <td>99.47%</td> </tr> <tr> <td>VQAv2 (val)<br><i>vqa_match</i></td> <td>82.51</td> <td>82.50</td> <td>99.99%</td> </tr> <tr> <td>DocVQA (val)<br><i>anls</i></td> <td>95.01</td> <td>94.90</td> <td>99.88%</td> </tr> <tr> <td>ChartQA (test, CoT)<br><i>anywhere_in_answer_relaxed_correctness</i></td> <td>83.40</td> <td>83.32</td> <td>99.90%</td> </tr> <tr> <td>Mathvista (testmini, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td> <td>66.57</td> <td>69.57</td> <td>104.51%</td> </tr> <tr> <td><b>Average Score</b></td> <td><b>77.12</b></td> <td><b>77.21</b></td> <td><b>100.12%</b></td> </tr> <tr> <td rowspan="2"><b>Text</b></td> <td>MGSM (CoT)</td> <td>68.60</td> <td>67.62</td> <td>98.57%</td> </tr> <tr> <td>MMLU (5-shot)</td> <td>82.70</td> <td>82.83</td> <td>100.16%</td> </tr> </tbody> </table>

Inference Performance

This model achieves up to 2.0x speedup in single-stream deployment and up to 1.87x 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.

<details> <summary>Benchmarking Command</summary> ``` guidellm --model neuralmagic/Qwen2-VL-72B-Instruct-quantized.w8a8 --target "http://localhost:8000/v1" --data-type emulated --data prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>,images=<num_images>,width=<image_width>,height=<image_height> --max seconds 120 --backend aiohttp_server ``` </details>

Single-stream performance (measured with vLLM version 0.7.2)

<table border="1" class="dataframe"> <thead> <tr> <th></th> <th></th> <th></th> <th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th> <th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th> <th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th> </tr> <tr> <th>Hardware</th> <th>Number of GPUs</th> <th>Model</th> <th>Average Cost Reduction</th> <th>Latency (s)</th> <th>QPD</th> <th>Latency (s)th> <th>QPD</th> <th>Latency (s)</th> <th>QPD</th> </tr> </thead> <tbody> <tr> <th rowspan="3" valign="top">A100</th> <td>4</td> <td>Qwen/Qwen2-VL-72B-Instruct</td> <td></td> <td>6.5</td> <td>77</td> <td>4.6</td> <td>110</td> <td>4.4</td> <td>113</td> </tr> <tr> <td>2</td> <td>neuralmagic/Qwen2-VL-72B-Instruct-quantized.w8a8</td> <td>1.85</td> <td>7.2</td> <td>139</td> <td>4.9</td> <td>206</td> <td>4.8</td> <td>211</td> </tr> <tr> <td>1</td> <td>neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16</td> <td>3.32</td> <td>10.0</td> <td>202</td> <td>5.0</td> <td>398</td> <td>4.8</td> <td>419</td> </tr> <tr> <th rowspan="3" valign="top">H100</td> <td>4</td> <td>Qwen/Qwen2-VL-72B-Instruct</td> <td></td> <td>4.4</td> <td>66</td> <td>3.0</td> <td>97</td> <td>2.9</td> <td>99</td> </tr> <tr> <td>2</td> <td>neuralmagic/Qwen2-VL-72B-Instruct-FP8-Dynamic</td> <td>1.79</td> <td>4.7</td> <td>119</td> <td>3.3</td> <td>173</td> <td>3.2</td> <td>177</td> </tr> <tr> <td>1</td> <td>neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16</td> <td>2.60</td> <td>6.4</td> <td>172</td> <td>4.3</td> <td>253</td> <td>4.2</td> <td>259</td> </tr> </tbody> </table>

**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)

<table border="1" class="dataframe"> <thead> <tr> <th></th> <th></th> <th></th> <th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th> <th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th> <th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th> </tr> <tr> <th>Hardware</th> <th>Model</th> <th>Average Cost Reduction</th> <th>Maximum throughput (QPS)</th> <th>QPD</th> <th>Maximum throughput (QPS)</th> <th>QPD</th> <th>Maximum throughput (QPS)</th> <th>QPD</th> </tr> </thead> <tbody> <tr> <th rowspan="3" valign="top">A100x4</th> <td>Qwen/Qwen2-VL-72B-Instruct</td> <td></td> <td>0.3</td> <td>169</td> <td>1.1</td> <td>538</td> <td>1.2</td> <td>595</td> </tr> <tr> <td>neuralmagic/Qwen2-VL-72B-Instruct-quantized.w8a8</td> <td>1.84</td> <td>0.6</td> <td>293</td> <td>2.0</td> <td>1021</td> <td>2.3</td> <td>1135</td> </tr> <tr> <td>neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16</td> <td>2.73</td> <td>0.6</td> <td>314</td> <td>3.2</td> <td>1591</td> <td>4.0</td> <td>2019</td> </tr> <tr> <th rowspan="3" valign="top">H100x4</td> <td>Qwen/Qwen2-VL-72B-Instruct</td> <td></td> <td>0.5</td> <td>137</td> <td>1.2</td> <td>356</td> <td>1.3</td> <td>377</td> </tr> <tr> <td>neuralmagic/Qwen2-VL-72B-Instruct-FP8-Dynamic</td> <td>1.70</td> <td>0.8</td> <td>236</td> <td>2.2</td> <td>623</td> <td>2.4</td> <td>669</td> </tr> <tr> <td>neuralmagic/Qwen2-VL-72B-Instruct-quantized.w4a16</td> <td>2.35</td> <td>1.3</td> <td>350</td> <td>3.3</td> <td>910</td> <td>3.6</td> <td>994</td> </tr> </tbody> </table>

**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).