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  1. .gitattributes +58 -0
  2. Qwen3-VL-8B-Instruct-AX650-c128_p1152/Qwen3-VL-8B-Instruct_vision.axmodel +3 -0
  3. Qwen3-VL-8B-Instruct-AX650-c128_p1152/model.embed_tokens.weight.bfloat16.bin +3 -0
  4. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l0_together.axmodel +3 -0
  5. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l10_together.axmodel +3 -0
  6. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l11_together.axmodel +3 -0
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  8. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l13_together.axmodel +3 -0
  9. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l14_together.axmodel +3 -0
  10. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l15_together.axmodel +3 -0
  11. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l16_together.axmodel +3 -0
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  15. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l1_together.axmodel +3 -0
  16. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l20_together.axmodel +3 -0
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  19. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l23_together.axmodel +3 -0
  20. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l24_together.axmodel +3 -0
  21. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l25_together.axmodel +3 -0
  22. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l26_together.axmodel +3 -0
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  26. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l2_together.axmodel +3 -0
  27. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l30_together.axmodel +3 -0
  28. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l31_together.axmodel +3 -0
  29. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l32_together.axmodel +3 -0
  30. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l33_together.axmodel +3 -0
  31. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l34_together.axmodel +3 -0
  32. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l35_together.axmodel +3 -0
  33. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l3_together.axmodel +3 -0
  34. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l4_together.axmodel +3 -0
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  37. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l7_together.axmodel +3 -0
  38. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l8_together.axmodel +3 -0
  39. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_p128_l9_together.axmodel +3 -0
  40. Qwen3-VL-8B-Instruct-AX650-c128_p1152/qwen3_vl_text_post.axmodel +3 -0
  41. README.md +233 -3
  42. config.json +0 -0
  43. images/demo.jpg +3 -0
  44. images/demo1.jpg +3 -0
  45. images/recoAll_attractions_1.jpg +3 -0
  46. images/recoAll_attractions_2.jpg +3 -0
  47. images/recoAll_attractions_3.jpg +3 -0
  48. images/recoAll_attractions_4.jpg +3 -0
  49. images/ssd_car.jpg +3 -0
  50. images/ssd_horse.jpg +3 -0
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README.md CHANGED
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1
- ---
2
- license: mit
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ language:
4
+ - en
5
+ - zh
6
+ base_model:
7
+ - Qwen/Qwen3-VL-2B-Instruct
8
+ - Qwen/Qwen3-VL-4B-Instruct
9
+ - Qwen/Qwen3-VL-8B-Instruct
10
+ pipeline_tag: image-text-to-text
11
+ library_name: transformers
12
+ tags:
13
+ - Qwen3-VL
14
+ - Qwen3-VL-2B-Instruct
15
+ - Qwen3-VL-4B-Instruct
16
+ - Qwen3-VL-8B-Instruct
17
+ - Int8
18
+ - VLM
19
+ - GPTQ
20
+ ---
21
+
22
+ # Qwen3-VL
23
+
24
+ This version of Qwen3-VL-2B-Instruct has been converted to run on the Axera NPU using **w8a16** quantization.
25
+
26
+ Compatible with Pulsar2 version: 5.0
27
+
28
+ ## Convert tools links:
29
+
30
+ For those who are interested in model conversion, you can try to export axmodel through the original repo :
31
+
32
+ - https://huggingface.co/Qwen/Qwen3-VL-2B-Instruct
33
+ - https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct
34
+
35
+ [Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html)
36
+
37
+ [AXera NPU HOST LLM Runtime](https://github.com/AXERA-TECH/Qwen3-VL.AXERA)
38
+
39
+
40
+ ## Support Platform
41
+
42
+ - AX650
43
+ - AX650N DEMO Board
44
+ - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
45
+ - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
46
+
47
+ **Image Process**
48
+ |Chips| input size | image num | image encoder | ttft(168 tokens) | w8a16 | CMM | Flash |
49
+ |--|--|--|--|--|--|--|--|
50
+ |AX650| 384*384 | 1 | ms | ms | tokens/sec| GiB | 14 GiB |
51
+
52
+ **Video Process**
53
+ |Chips| input size | image num | image encoder |ttft(600 tokens) | w8a16 | CMM | Flash |
54
+ |--|--|--|--|--|--|--|--|
55
+ |AX650| 384*384 | 8 | ms | ms | tokens/sec| GiB | 14 GiB |
56
+
57
+ The DDR capacity refers to the CMM memory that needs to be consumed. Ensure that the CMM memory allocation on the development board is greater than this value.
58
+
59
+ ## How to use
60
+
61
+ Download all files from this repository to the device
62
+
63
+ **If you using AX650 Board**
64
+
65
+ ### Prepare tokenizer server
66
+
67
+ #### Install transformer
68
+
69
+ ```
70
+ pip install -r requirements.txt
71
+ ```
72
+
73
+ ### Demo Run
74
+
75
+ #### Image understand demo
76
+
77
+ ##### start tokenizer server for image understand demo
78
+
79
+ ```
80
+ python3 tokenizer_images.py --port 8080
81
+ ```
82
+
83
+ ##### run image understand demo
84
+
85
+ - input text
86
+
87
+ ```
88
+ 描述这张图片
89
+ ```
90
+
91
+ - input image
92
+
93
+ ![](./images/recoAll_attractions_1.jpg)
94
+
95
+ ```
96
+ root@ax650 ~/Qwen3-VL-2B-Instruct-GPTQ-Int4 # bash run_image_ax650.sh
97
+ [I][ Init][ 156]: LLM init start
98
+ [I][ Init][ 158]: Total CMM:4353 MB
99
+ [I][ Init][ 34]: connect http://127.0.0.1:8080 ok
100
+ bos_id: -1, eos_id: 151645
101
+ img_start_token: 151652
102
+ img_context_token: 151655
103
+ 3% | ██ | 1 / 31 [0.01s<0.46s, 66.67 count/s] tokenizer init ok[I][ Init][ 26]: LLaMaEmbedSelector use mmap
104
+ 6% | ███ | 2 / 31 [0.02s<0.34s, 90.91 count/s] embed_selector init ok[I][ Init][ 201]: attr.axmodel_num:28
105
+ 103% | ██████████████████████████████████ | 32 / 31 [34.03s<32.96s, 0.94 count/s] init vpm axmodel ok,remain_cmm(854 MB)[I][ Init][ 266]: IMAGE_CONTEXT_TOKEN: 151655, IMAGE_START_TOKEN: 151652
106
+ [I][ Init][ 309]: image encoder output float32
107
+
108
+ [I][ Init][ 339]: max_token_len : 2047
109
+ [I][ Init][ 344]: kv_cache_size : 1024, kv_cache_num: 2047
110
+ [I][ Init][ 352]: prefill_token_num : 128
111
+ [I][ Init][ 356]: grp: 1, prefill_max_token_num : 1
112
+ [I][ Init][ 356]: grp: 2, prefill_max_token_num : 128
113
+ [I][ Init][ 356]: grp: 3, prefill_max_token_num : 256
114
+ [I][ Init][ 356]: grp: 4, prefill_max_token_num : 384
115
+ [I][ Init][ 356]: grp: 5, prefill_max_token_num : 512
116
+ [I][ Init][ 356]: grp: 6, prefill_max_token_num : 640
117
+ [I][ Init][ 356]: grp: 7, prefill_max_token_num : 768
118
+ [I][ Init][ 356]: grp: 8, prefill_max_token_num : 896
119
+ [I][ Init][ 356]: grp: 9, prefill_max_token_num : 1024
120
+ [I][ Init][ 356]: grp: 10, prefill_max_token_num : 1152
121
+ [I][ Init][ 360]: prefill_max_token_num : 1152
122
+ [I][ Init][ 372]: LLM init ok
123
+ [I][ Init][ 374]: Left CMM:854 MB
124
+ Type "q" to exit, Ctrl+c to stop current running
125
+ prompt >> 描述这张图片
126
+ image >> images/recoAll_attractions_1.jpg
127
+ [I][ EncodeImage][ 440]: pixel_values size 1
128
+ [I][ EncodeImage][ 441]: grid_h 24 grid_w 24
129
+ [I][ EncodeImage][ 489]: image encode time : 237.778000 ms, size : 1
130
+ [I][ Encode][ 532]: input_ids size:168
131
+ [I][ Encode][ 540]: offset 15
132
+ [I][ Encode][ 569]: img_embed.size:1, 294912
133
+ [I][ Encode][ 583]: out_embed size:344064
134
+ [I][ Encode][ 584]: input_ids size 168
135
+ [I][ Encode][ 586]: position_ids size:168
136
+ [I][ Run][ 607]: input token num : 168, prefill_split_num : 2
137
+ [I][ Run][ 641]: input_num_token:128
138
+ [I][ Run][ 641]: input_num_token:40
139
+ [I][ Run][ 865]: ttft: 313.60 ms
140
+ 这是一张在埃及沙漠中拍摄的风景照片。画面中，三座巨大的金字塔在晴朗的天空下矗立，它们是古埃及文明的象征。这些金字塔由巨大的石块堆叠而成，表面因岁月侵蚀而显得斑驳。在金字塔的前方，有几个人影在沙地上行走，这为整个场景提供了比例感和尺度感。整个场景充满了历史的厚重感和神秘的氛围。
141
+
142
+ [N][ Run][ 992]: hit eos,avg 14.14 token/s
143
+ ```
144
+
145
+ #### Video understand demo
146
+
147
+ ##### start tokenizer server for image understand demo
148
+
149
+ ```
150
+ python tokenizer_video.py --port 8080
151
+ ```
152
+
153
+ ##### run video understand demo
154
+ - input text
155
+
156
+ ```
157
+ 描述这个视频
158
+ ```
159
+
160
+ - input video
161
+
162
+ ./video
163
+
164
+ ```
165
+ root@ax650 ~/Qwen3-VL-2B-Instruct-GPTQ-Int4 # bash run_video_ax650.sh
166
+ [I][ Init][ 156]: LLM init start
167
+ [I][ Init][ 158]: Total CMM:7884 MB
168
+ [I][ Init][ 34]: connect http://127.0.0.1:8080 ok
169
+ bos_id: -1, eos_id: 151645
170
+ img_start_token: 151652
171
+ img_context_token: 151656
172
+ 3% | ██ | 1 / 31 [0.01s<0.34s, 90.91 count/s] tokenizer init ok[I][ Init][ 26]: LLaMaEmbedSelector use mmap
173
+ 6% | ███ | 2 / 31 [0.01s<0.23s, 133.33 count/s] embed_selector init ok[I][ Init][ 201]: attr.axmodel_num:28
174
+ 103% | ██████████████████████████████████ | 32 / 31 [32.37s<31.36s, 0.99 count/s] init vpm axmodel ok,remain_cmm(4385 MB)[I][ Init][ 266]: IMAGE_CONTEXT_TOKEN: 151656, IMAGE_START_TOKEN: 151652
175
+ [I][ Init][ 309]: image encoder output float32
176
+
177
+ [I][ Init][ 339]: max_token_len : 2047
178
+ [I][ Init][ 344]: kv_cache_size : 1024, kv_cache_num: 2047
179
+ [I][ Init][ 352]: prefill_token_num : 128
180
+ [I][ Init][ 356]: grp: 1, prefill_max_token_num : 1
181
+ [I][ Init][ 356]: grp: 2, prefill_max_token_num : 128
182
+ [I][ Init][ 356]: grp: 3, prefill_max_token_num : 256
183
+ [I][ Init][ 356]: grp: 4, prefill_max_token_num : 384
184
+ [I][ Init][ 356]: grp: 5, prefill_max_token_num : 512
185
+ [I][ Init][ 356]: grp: 6, prefill_max_token_num : 640
186
+ [I][ Init][ 356]: grp: 7, prefill_max_token_num : 768
187
+ [I][ Init][ 356]: grp: 8, prefill_max_token_num : 896
188
+ [I][ Init][ 356]: grp: 9, prefill_max_token_num : 1024
189
+ [I][ Init][ 356]: grp: 10, prefill_max_token_num : 1152
190
+ [I][ Init][ 360]: prefill_max_token_num : 1152
191
+ [I][ Init][ 372]: LLM init ok
192
+ [I][ Init][ 374]: Left CMM:4385 MB
193
+ Type "q" to exit, Ctrl+c to stop current running
194
+ prompt >> 描述这个视频
195
+ video >> video
196
+ video/frame_0000.jpg
197
+ video/frame_0008.jpg
198
+ video/frame_0016.jpg
199
+ video/frame_0024.jpg
200
+ video/frame_0032.jpg
201
+ video/frame_0040.jpg
202
+ video/frame_0048.jpg
203
+ video/frame_0056.jpg
204
+ [I][ EncodeImage][ 440]: pixel_values size 4
205
+ [I][ EncodeImage][ 441]: grid_h 24 grid_w 24
206
+ [I][ EncodeImage][ 489]: image encode time : 751.481018 ms, size : 4
207
+ [I][ Encode][ 532]: input_ids size:600
208
+ [I][ Encode][ 540]: offset 15
209
+ [I][ Encode][ 569]: img_embed.size:4, 294912
210
+ [I][ Encode][ 574]: offset:159
211
+ [I][ Encode][ 574]: offset:303
212
+ [I][ Encode][ 574]: offset:447
213
+ [I][ Encode][ 583]: out_embed size:1228800
214
+ [I][ Encode][ 584]: input_ids size 600
215
+ [I][ Encode][ 586]: position_ids size:600
216
+ [I][ Run][ 607]: input token num : 600, prefill_split_num : 5
217
+ [I][ Run][ 641]: input_num_token:128
218
+ [I][ Run][ 641]: input_num_token:128
219
+ [I][ Run][ 641]: input_num_token:128
220
+ [I][ Run][ 641]: input_num_token:128
221
+ [I][ Run][ 641]: input_num_token:88
222
+ [I][ Run][ 865]: ttft: 843.36 ms
223
+ 这是一段关于两只山地旱獭（也称“山地土拨鼠”）在山地环境中互动的视频。
224
+
225
+ 在画面中，两只山地旱獭正站在布满碎石的山坡上，背景是连绵起伏的山脉和蓝天。它们的毛色以灰、棕、黑相间，脸部和耳朵周围有明显的黑白条纹，显得非常可爱。
226
+
227
+ 这两只旱獭正在进行一场激烈的“拳击”或“格斗”游戏。它们的前爪高高举起，像在互相击打，但它们的姿势和动作表明它们可能是在进行一场激烈的“拳击”或“格斗”游戏。它们的嘴巴和前爪在空中挥舞，似乎在互相攻击或展示力量。
228
+
229
+ 整个场景充满了动感和活力，展现了这些小动物在自然环境中充满活力和趣味的一面。
230
+
231
+ [N][ Run][ 992]: hit eos,avg 14.16 token/s
232
+
233
+ ```
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  • Size of remote file: 59.4 kB
images/recoAll_attractions_4.jpg ADDED

Git LFS Details

  • SHA256: e47e13a69c16073e221b143a43263fcbe956bcfeb8ad47fddbdbc03a2b6f7261
  • Pointer size: 131 Bytes
  • Size of remote file: 115 kB
images/ssd_car.jpg ADDED

Git LFS Details

  • SHA256: 92d459a39a9eef03956257cf9fec84114d9e5df8fb9c0662fb257488cdd4f365
  • Pointer size: 130 Bytes
  • Size of remote file: 50.5 kB
images/ssd_horse.jpg ADDED

Git LFS Details

  • SHA256: ed22f6b4c8c33e50e391e089ede14e8fa9402c623b09dbcf010e804770698fbb
  • Pointer size: 131 Bytes
  • Size of remote file: 123 kB