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Qwen2.5-VL-72B-Instruct-quantized.w4a16

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---
tags:
- vllm
- vision
- w4a16
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-72B-Instruct
library_name: transformers
---
# Qwen2.5-VL-72B-Instruct-quantized-w4a16
## Model Overview
- **Model Architecture:** Qwen/Qwen2.5-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/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct).
### Model Optimizations
This model was obtained by quantizing the weights of [Qwen/Qwen2.5-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-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](https://docs.vllm.ai/en/latest/) 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-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](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created with [llm-compressor](https://github.com/vllm-project/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,
)
from compressed_tensors.quantization import QuantizationArgs, QuantizationType, QuantizationStrategy, ActivationOrdering, QuantizationScheme
# Load model.
model_id = "Qwen/Qwen2.5-VL-72B-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 = 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=["Qwen2_5_VLDecoderLayer"],
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=data_collator,
output_dir=SAVE_DIR
)
```
</details>
## Evaluation
The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard), OpenLLM Leaderboard [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/) and on [HumanEval](https://github.com/neuralmagic/evalplus), using the following commands:
<details>
<summary>Evaluation Commands</summary>
```
```
</details>
### 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](https://docs.vllm.ai/en/latest/) version 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm).
<details>
<summary>Benchmarking Command</summary>
```
guidellm --model neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16 --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>
<tr>
<td>A100x4</td>
<td>Qwen/Qwen2.5-VL-72B-Instruct</td>
<td></td>
<td>6.4</td>
<td>78</td>
<td>4.5</td>
<td>111</td>
<td>4.4</td>
<td>113</td>
</tr>
<tr>
<td>A100x2</td>
<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w8a8</td>
<td>1.85</td>
<td>7.0</td>
<td>143</td>
<td>4.9</td>
<td>205</td>
<td>4.8</td>
<td>211</td>
</tr>
<tr>
<td>A100x1</td>
<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</td>
<td>3.33</td>
<td>9.4</td>
<td>213</td>
<td>5.1</td>
<td>396</td>
<td>4.8</td>
<td>420</td>
</tr>
<tr>
<td>H100x4</td>
<td>Qwen/Qwen2.5-VL-72B-Instruct</td>
<td></td>
<td>4.3</td>
<td>68</td>
<td>3.0</td>
<td>97</td>
<td>2.9</td>
<td>100</td>
</tr>
<tr>
<td>H100x2</td>
<td>neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic</td>
<td>1.79</td>
<td>4.6</td>
<td>122</td>
<td>3.3</td>
<td>173</td>
<td>3.2</td>
<td>177</td>
</tr>
<tr>
<td>H100x1</td>
<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</td>
<td>5.66</td>
<td>4.3</td>
<td>252</td>
<td>4.3</td>
<td>252</td>
<td>1.0</td>
<td>1065</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](https://lambdalabs.com/service/gpu-cloud) (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>
<td>A100x4</td>
<td>Qwen/Qwen2.5-VL-72B-Instruct</td>
<td></td>
<td>0.4</td>
<td>180</td>
<td>1.1</td>
<td>539</td>
<td>1.2</td>
<td>595</td>
</tr>
<tr>
<td>A100x2</td>
<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w8a8</td>
<td>1.80</td>
<td>0.6</td>
<td>289</td>
<td>2.0</td>
<td>1020</td>
<td>2.3</td>
<td>1133</td>
</tr>
<tr>
<td>A100x1</td>
<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</td>
<td>2.75</td>
<td>0.7</td>
<td>341</td>
<td>3.2</td>
<td>1588</td>
<td>4.1</td>
<td>2037</td>
</tr>
<tr>
<td>H100x4</td>
<td>Qwen/Qwen2.5-VL-72B-Instruct</td>
<td></td>
<td>0.5</td>
<td>134</td>
<td>1.2</td>
<td>357</td>
<td>1.3</td>
<td>379</td>
</tr>
<tr>
<td>H100x2</td>
<td>neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic</td>
<td>1.73</td>
<td>0.9</td>
<td>247</td>
<td>2.2</td>
<td>621</td>
<td>2.4</td>
<td>669</td>
</tr>
<tr>
<td>H100x1</td>
<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</td>
<td>8.27</td>
<td>3.3</td>
<td>913</td>
<td>3.3</td>
<td>913</td>
<td>24.8</td>
<td>6777</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](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).