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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-VL-72B-Instruct
library_name: transformers
---
# Qwen2-VL-72B-Instruct-quantized-w4a16
## Model Overview
- **Model Architecture:** Qwen/Qwen2-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-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct).
### Model Optimizations
This model was obtained by quantizing the weights of [Qwen/Qwen2-VL-72B-Instruct](https://huggingface.co/Qwen/Qwen2-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-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
from transformers import AutoProcessor, Qwen2VLForConditionalGeneration
from llmcompressor.modifiers.quantization import QuantizationModifier
from llmcompressor.transformers import oneshot, wrap_hf_model_class
MODEL_ID = "Qwen/Qwen2-VL-72B-Instruct"
# Load model.
model_class = wrap_hf_model_class(Qwen2VLForConditionalGeneration)
model = model_class.from_pretrained(MODEL_ID, device_map="auto", torch_dtype="auto")
processor = AutoProcessor.from_pretrained(MODEL_ID)
# Configure the quantization algorithm and scheme.
# In this case, we:
# * quantize the weights to fp8 with per channel via ptq
# * quantize the activations to fp8 with dynamic per token
recipe = QuantizationModifier(
targets="Linear",
scheme="FP8_DYNAMIC",
ignore=["re:.*lm_head", "re:visual.*"],
)
# Apply quantization and save to disk in compressed-tensors format.
SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8-dynamic"
oneshot(model=model, recipe=recipe, output_dir=SAVE_DIR)
processor.save_pretrained(SAVE_DIR)
# Confirm generations of the quantized model look sane.
print("========== SAMPLE GENERATION ==============")
input_ids = processor(text="Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=20)
print(processor.decode(output[0]))
print("==========================================")
```
</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 \
--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-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>62.11</td>
<td>60.67</td>
<td>97.68%</td>
</tr>
<tr>
<td>VQAv2 (val)<br><i>vqa_match</i></td>
<td>82.51</td>
<td>82.44</td>
<td>99.91%</td>
</tr>
<tr>
<td>DocVQA (val)<br><i>anls</i></td>
<td>95.01</td>
<td>95.10</td>
<td>100.09%</td>
</tr>
<tr>
<td>ChartQA (test, CoT)<br><i>anywhere_in_answer_relaxed_correctness</i></td>
<td>83.40</td>
<td>83.68</td>
<td>100.34%</td>
</tr>
<tr>
<td>Mathvista (testmini, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
<td>66.57</td>
<td>67.07</td>
<td>100.75%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>77.12</b></td>
<td><b>77.39</b></td>
<td><b>100.35%</b></td>
</tr>
<tr>
<td rowspan="2"><b>Text</b></td>
<td>MGSM (CoT)</td>
<td>68.60</td>
<td>67.78</td>
<td>98.80%</td>
</tr>
<tr>
<td>MMLU (5-shot)</td>
<td>82.70</td>
<td>82.60</td>
<td>99.88%</td>
</tr>
</tbody>
</table>
## Inference Performance
This model achieves up to 1.84x speedup in single-stream deployment and up to 1.85x 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-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 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](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>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](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).