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---
tags:
- vllm
- vision
- fp8
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-FP8-Dynamic
## Model Overview
- **Model Architecture:** Qwen2.5-VL-72B-Instruct
- **Input:** Vision-Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** FP8
- **Activation quantization:** FP8
- **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 FP8 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-FP8-Dynamic",
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 requests
import torch
from PIL import Image
from transformers import AutoProcessor
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.tracing import (
TraceableQwen2_5_VLForConditionalGeneration,
)
from llmcompressor.modifiers.quantization import QuantizationModifier
# 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)
# Recipe
recipe = [
QuantizationModifier(
targets="Linear",
scheme="FP8_DYNAMIC",
sequential_targets=["MistralDecoderLayer"],
ignore=["re:.*lm_head", "re:vision_tower.*", "re:multi_modal_projector.*"],
),
]
SAVE_DIR=f"{model_id.split('/')[1]}-FP8-Dynamic"
# Perform oneshot
oneshot(
model=model,
recipe=recipe,
trust_remote_code_model=True,
output_dir=SAVE_DIR
)
```
</details>
## Evaluation
The model was evaluated using [mistral-evals](https://github.com/neuralmagic/mistral-evals) for vision-related tasks and using [lm_evaluation_harness](https://github.com/neuralmagic/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 \
--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-72B-Instruct</th>
<th>neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic</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>64.33</td>
<td>66.88</td>
<td>103.96%</td>
</tr>
<tr>
<td>VQAv2 (val)<br><i>vqa_match</i></td>
<td>81.94</td>
<td>81.94</td>
<td>100.00%</td>
</tr>
<tr>
<td>DocVQA (val)<br><i>anls</i></td>
<td>94.71</td>
<td>94.64</td>
<td>99.93%</td>
</tr>
<tr>
<td>ChartQA (test, CoT)<br><i>anywhere_in_answer_relaxed_correctness</i></td>
<td>88.96</td>
<td>89.04</td>
<td>100.09%</td>
</tr>
<tr>
<td>Mathvista (testmini, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
<td>78.18</td>
<td>77.78</td>
<td>99.49%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>81.62</b></td>
<td><b>81.86</b></td>
<td><b>100.29%</b></td>
</tr>
<tr>
<td rowspan="2"><b>Text</b></td>
<td>MGSM (CoT)</td>
<td>75.45</td>
<td>49.65</td>
<td>65.81%</td>
</tr>
<tr>
<td>MMLU (5-shot)</td>
<td>86.16</td>
<td>86.12</td>
<td>99.95%</td>
</tr>
</tbody>
</table>
## Inference Performance
This model achieves up to 1.79x speedup in single-stream deployment and up to 1.84x 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-FP8-Dynamic --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></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>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>
<th rowspan="3" valign="top">A100</td>
<td>4</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>2</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>1</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>
<th rowspan="3" valign="top">H100</td>
<td>4</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>2</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>1</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>
<th rowspan="3" valign="top">A100x4</th>
<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>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w8a8</td>
<td>1.80</td>
<td>1.2</td>
<td>578</td>
<td>4.0</td>
<td>2040</td>
<td>4.6</td>
<td>2266</td>
</tr>
<tr>
<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</td>
<td>2.75</td>
<td>2.8</td>
<td>1364</td>
<td>12.8</td>
<td>6352</td>
<td>16.4</td>
<td>8148</td>
</tr>
<tr>
<th rowspan="3" valign="top">H100x4</th>
<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>neuralmagic/Qwen2.5-VL-72B-Instruct-FP8-Dynamic</td>
<td>1.73</td>
<td>1.8</td>
<td>479</td>
<td>4.4</td>
<td>1203</td>
<td>4.8</td>
<td>1296</td>
</tr>
<tr>
<td>neuralmagic/Qwen2.5-VL-72B-Instruct-quantized.w4a16</td>
<td>8.27</td>
<td>13.2</td>
<td>3652</td>
<td>13.2</td>
<td>3652</td>
<td>99.2</td>
<td>27108</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).