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

sourceHugging Faceapache-2.0updated 1y agoView on Hugging Face

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

Model Overview

—Model Architecture: Qwen/Qwen2.5-VL-7B-Instruct
—Input: Text/Image/Video
—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.

python

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.

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

python

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
)

</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/Qwen2.5-VL-7B-Instruct</th> <th>Qwen2.5-VL-7B-Instruct-quantized.w8a8</th> <th>Recovery (%)</th> </tr> </thead> <tbody> <tr> <td rowspan="6"><b>Vision</b></td> <td>MMMU (val, CoT)<br><i>explicitpromptrelaxedcorrectness</i></td> <td>52.00</td> <td>52.33</td> <td>100.63%</td> </tr> <tr> <td>VQAv2 (val)<br><i>vqamatch</i></td> <td>75.59</td> <td>75.46</td> <td>99.83%</td> </tr> <tr> <td>DocVQA (val)<br><i>anls</i></td> <td>94.27</td> <td>94.09</td> <td>99.81%</td> </tr> <tr> <td>ChartQA (test, CoT)<br><i>anywhereinanswerrelaxedcorrectness</i></td> <td>86.44</td> <td>86.16</td> <td>99.68%</td> </tr> <tr> <td>Mathvista (testmini, CoT)<br><i>explicitpromptrelaxed_correctness</i></td> <td>69.47</td> <td>70.47</td> <td>101.44%</td> </tr> <tr> <td><b>Average Score</b></td> <td><b>75.95</b></td> <td><b>75.90</b></td> <td><b>99.93%</b></td> </tr> <tr> <td rowspan="3"><b>Text</b></td> <td>MGSM (CoT)</td> <td>56.38</td> <td>55.13</td> <td>97.78%</td> </tr> <tr> <td>MMLU (5-shot)</td> <td>71.09</td> <td>70.57</td> <td>99.27%</td> </tr> </tbody> </table>

Inference Performance

This model achieves up to 1.56x speedup in single-stream deployment and 1.5x in multi-stream 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/Qwen2.5-VL-7B-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>Model</th> <th>Average Cost Reduction</th> <th>Latency (s)</th> <th>Queries Per Dollar</th> <th>Latency (s)th> <th>Queries Per Dollar</th> <th>Latency (s)</th> <th>Queries Per Dollar</th> </tr> </thead> <tbody style="text-align: center"> <tr> <th rowspan="3" valign="top">A6000x1</th> <th>Qwen/Qwen2.5-VL-7B-Instruct</th> <td></td> <td>4.9</td> <td>912</td> <td>3.2</td> <td>1386</td> <td>3.1</td> <td>1431</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a8</th> <td>1.50</td> <td>3.6</td> <td>1248</td> <td>2.1</td> <td>2163</td> <td>2.0</td> <td>2237</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16</th> <td>2.05</td> <td>3.3</td> <td>1351</td> <td>1.4</td> <td>3252</td> <td>1.4</td> <td>3321</td> </tr> <tr> <th rowspan="3" valign="top">A100x1</th> <th>Qwen/Qwen2.5-VL-7B-Instruct</th> <td></td> <td>2.8</td> <td>707</td> <td>1.7</td> <td>1162</td> <td>1.7</td> <td>1198</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a8</th> <td>1.24</td> <td>2.4</td> <td>851</td> <td>1.4</td> <td>1454</td> <td>1.3</td> <td>1512</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16</th> <td>1.49</td> <td>2.2</td> <td>912</td> <td>1.1</td> <td>1791</td> <td>1.0</td> <td>1950</td> </tr> <tr> <th rowspan="3" valign="top">H100x1</th> <th>Qwen/Qwen2.5-VL-7B-Instruct</th> <td></td> <td>2.0</td> <td>557</td> <td>1.2</td> <td>919</td> <td>1.2</td> <td>941</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-FP8-Dynamic</th> <td>1.28</td> <td>1.6</td> <td>698</td> <td>0.9</td> <td>1181</td> <td>0.9</td> <td>1219</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16</th> <td>1.28</td> <td>1.6</td> <td>686</td> <td>0.9</td> <td>1191</td> <td>0.9</td> <td>1228</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>Queries Per Dollar</th> <th>Maximum throughput (QPS)</th> <th>Queries Per Dollar</th> <th>Maximum throughput (QPS)</th> <th>Queries Per Dollar</th> </tr> </thead> <tbody style="text-align: center"> <tr> <th rowspan="3" valign="top">A6000x1</th> <th>Qwen/Qwen2.5-VL-7B-Instruct</th> <td></td> <td>0.4</td> <td>1837</td> <td>1.5</td> <td>6846</td> <td>1.7</td> <td>7638</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a8</th> <td>1.41</td> <td>0.5</td> <td>2297</td> <td>2.3</td> <td>10137</td> <td>2.5</td> <td>11472</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16</th> <td>1.60</td> <td>0.4</td> <td>1828</td> <td>2.7</td> <td>12254</td> <td>3.4</td> <td>15477</td> </tr> <tr> <th rowspan="3" valign="top">A100x1</th> <th>Qwen/Qwen2.5-VL-7B-Instruct</th> <td></td> <td>0.7</td> <td>1347</td> <td>2.6</td> <td>5221</td> <td>3.0</td> <td>6122</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w8a8</th> <td>1.27</td> <td>0.8</td> <td>1639</td> <td>3.4</td> <td>6851</td> <td>3.9</td> <td>7918</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16</th> <td>1.21</td> <td>0.7</td> <td>1314</td> <td>3.0</td> <td>5983</td> <td>4.6</td> <td>9206</td> </tr> <tr> <th rowspan="3" valign="top">H100x1</th> <th>Qwen/Qwen2.5-VL-7B-Instruct</th> <td></td> <td>0.9</td> <td>969</td> <td>3.1</td> <td>3358</td> <td>3.3</td> <td>3615</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-FP8-Dynamic</th> <td>1.29</td> <td>1.2</td> <td>1331</td> <td>3.8</td> <td>4109</td> <td>4.2</td> <td>4598</td> </tr> <tr> <th>neuralmagic/Qwen2.5-VL-7B-Instruct-quantized.w4a16</th> <td>1.28</td> <td>1.2</td> <td>1298</td> <td>3.8</td> <td>4190</td> <td>4.2</td> <td>4573</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).