Hugging Face's logo

neuralmagic
/
Qwen2.5-VL-7B-Instruct-FP8-Dynamic

Image-Text-to-Text
Transformers
Safetensors
English
qwen2_5_vl
vllm
vision
fp8
conversational
text-generation-inference
compressed-tensors
Model card Files Community
4
Qwen2.5-VL-7B-Instruct-FP8-Dynamic
File size: 8,809 Bytes 
06f57b4
 
 
 
 
 
 
 
 
 
 
 
 
 
9c998f3
06f57b4
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
 
---
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-7B-Instruct
library_name: transformers
---

# Qwen2.5-VL-7B-Instruct-FP8-Dynamic

## Model Overview
- **Model Architecture:** Qwen2.5-VL-7B-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-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).

### Model Optimizations

This model was obtained by quantizing the weights of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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-7B-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-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)

# 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 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-7B-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>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 style="text-align: center">
    <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>
 


### 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 style="text-align: center">
     <tr>
      <th rowspan="3" valign="top">A100x1</th>
      <th>Qwen/Qwen2.5-VL-7B-Instruct-quantized.</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>