Qwen2.5
Collection
10 items
•
Updated
Qwen2.5-VL-3B-Instruct
This version of Qwen2.5-VL-3B-Instruct has been converted to run on the Axera NPU using w8a16 quantization.
This model has been optimized with the following LoRA:
Compatible with Pulsar2 version: 3.4
Convert tools links:
For those who are interested in model conversion, you can try to export axmodel through the original repo : https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct
Pulsar2 Link, How to Convert LLM from Huggingface to axmodel
Support Platform
- AX650
- AX650N DEMO Board
- M4N-Dock(爱芯派Pro)
- M.2 Accelerator card
Image Process
| Chips | input size | image num | image encoder | ttft(320 tokens) | w8a16 | DDR | Flash |
|---|---|---|---|---|---|---|---|
| AX650 | 448*448 | 1 | 780 ms | 2857 ms | 6.2 tokens/sec | 4.3 GiB | 4.6 GiB |
Video Process
| Chips | input size | image num | image encoder | ttft(512 tokens) | w8a16 | DDR | Flash |
|---|---|---|---|---|---|---|---|
| AX650 | 308*308 | 8 | 1400 ms | 5400 ms | 6.1 tokens/sec | 4.4 GiB | 4.7 GiB |
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.
How to use
Download all files from this repository to the device
If you using AX650 Board
root@ax650:/mnt/qtang/llm-test/qwen2.5-vl-3b# tree -L 2
.
├── image
│ └── ssd_car.jpg
├── main
├── main_axcl_x86
├── main_axcl_aarch64
├── python
│ ├── cv_resize.py
│ ├── infer_image.py
│ ├── infer_text.py
│ ├── infer_video.py
│ ├── preprocess.py
│ └── utils.py
├── qwen2_5-vl-3b-image-ax650
│ ├── Qwen2.5-VL-3B-Instruct_vision_nchw448.axmodel
│ ├── model.embed_tokens.weight.bfloat16.bin
│ ├── qwen2_5_vl_p320_l0_together.axmodel
......
│ ├── qwen2_5_vl_p320_l9_together.axmodel
│ └── qwen2_5_vl_post.axmodel
├── qwen2_5-vl-3b-video-ax650
│ ├── Qwen2.5-VL-3B-Instruct_vision_nhwc.axmodel
│ ├── model.embed_tokens.weight.bfloat16.bin
│ ├── qwen2_5_vl_p512_l0_together.axmodel
......
│ ├── qwen2_5_vl_p512_l9_together.axmodel
│ └── qwen2_5_vl_post.axmodel
├── qwen2_5-vl-tokenizer
│ ├── chat_template.json
│ ├── config.json
│ ├── generation_config.json
│ ├── merges.txt
│ ├── model.safetensors.index.json
│ ├── preprocessor_config.json
│ ├── tokenizer.json
│ ├── tokenizer_config.json
│ └── vocab.json
├── qwen2_tokenizer_image_448.py
├── qwen2_tokenizer_video_308.py
├── run_qwen2_5_vl_image.sh
├── run_qwen2_5_vl_video.sh
├── run_qwen2_5_vl_image_axcl_x86.sh
├── run_qwen2_5_vl_image_axcl_aarch64.sh
├── run_qwen2_5_vl_video_axcl_x86.sh
├── run_qwen2_5_vl_video_axcl_aarch64.sh
└── video
├── frame_0075.jpg
......
└── frame_0089.jpg
Prepare tokenizer server
Install transformer
pip install transformers==4.41.1 jinja2
Demo Run
Image understand demo
start tokenizer server for image understand demo
python3 qwen2_tokenizer_image_448.py --port 12345
run image understand demo
- input text
描述下图片
- input image
root@ax650:/mnt/qtang/llm-test/qwen2.5-vl-3b# ./run_qwen2_5_vl_image.sh
[I][ Init][ 129]: LLM init start
bos_id: -1, eos_id: 151645
2% | █ | 1 / 40 [0.01s<0.24s, 166.67 count/s] tokenizer init ok
[I][ Init][ 26]: LLaMaEmbedSelector use mmap
100% | ████████████████████████████████ | 40 / 40 [38.23s<38.23s, 1.05 count/s] init vpm axmodel ok,remain_cmm(7600 MB)
[I][ Init][ 277]: max_token_len : 1023
[I][ Init][ 282]: kv_cache_size : 256, kv_cache_num: 1023
[I][ Init][ 290]: prefill_token_num : 320
[I][ Init][ 292]: vpm_height : 1024,vpm_width : 392
[I][ Init][ 301]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
prompt >> who are you?
image >>
[I][ Run][ 638]: ttft: 2854.47 ms
I am a large language model created by Alibaba Cloud. I am called Qwen.
[N][ Run][ 779]: hit eos,avg 6.05 token/s
prompt >> 描述下图片
image >> image/ssd_car.jpg
[I][ Encode][ 416]: image encode time : 795.614014 ms, size : 524288
[I][ Run][ 638]: ttft: 2856.88 ms
这张图片展示了一条繁忙的城市街道。前景中，一名女子站在人行道上，她穿着黑色外套，面带微笑。她旁边是一辆红色的双层巴士，巴士上有一个广告，
上面写着“THINGS GET MORE EXITING WHEN YOU SAY ‘YES’”。巴士的车牌号是“L15”。巴士旁边停着一辆黑色的小型货车。背景中可以看到一些商店和行人，
街道两旁的建筑物是现代的玻璃幕墙建筑。整体氛围显得繁忙而充满活力。
[N][ Run][ 779]: hit eos,avg 5.96 token/s
Video understand demo
Please pre-process the image of the video file into a 308x308 size picture
start tokenizer server for image understand demo
python qwen2_tokenizer_video_308.py --port 12345
run image understand demo
root@ax650:/mnt/qtang/llm-test/qwen2.5-vl-3b# ./run_qwen2_5_vl_video.sh
[I][ Init][ 129]: LLM init start
bos_id: -1, eos_id: 151645
2% | █ | 1 / 40 [0.00s<0.12s, 333.33 count/s] tokenizer init ok
[I][ Init][ 26]: LLaMaEmbedSelector use mmap
100% | ████████████████████████████████ | 40 / 40 [40.05s<40.05s, 1.00 count/s] init vpm axmodel ok,remain_cmm(7680 MB)
[I][ Init][ 277]: max_token_len : 1023
[I][ Init][ 282]: kv_cache_size : 256, kv_cache_num: 1023
[I][ Init][ 290]: prefill_token_num : 512
[I][ Init][ 292]: vpm_height : 484,vpm_width : 392
[I][ Init][ 301]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
prompt >> 描述下视频
image >> video
video/frame_0000.jpg
video/frame_0008.jpg
video/frame_0016.jpg
video/frame_0024.jpg
video/frame_0032.jpg
video/frame_0040.jpg
video/frame_0048.jpg
video/frame_0056.jpg
[I][ Encode][ 416]: image encode time : 1487.557007 ms, size : 991232
[I][ Run][ 638]: ttft: 5488.29 ms
视频展示了两只松鼠在户外的场景。背景是模糊的山脉和蓝天，前景中有松鼠在互动。松鼠的毛色主要是棕色和白色，它们的爪子是橙色的。松鼠似乎在互相玩耍或争抢，它们的爪子和嘴巴都伸向对方。整个场景显得非常自然和生动。
Inference with M.2 Accelerator card
What is M.2 Accelerator card?, Show this DEMO based on Raspberry PI 5.
Image understand demo
start tokenizer server for image understand demo
python3 qwen2_tokenizer_image_448.py --port 12345
run image understand demo
- input text
描述这张图片
- input image
(base) axera@raspberrypi:~/lhj/Qwen2.5-VL-3B-Instruct $ bash run_qwen2_5_vl_image_axcl_aarch64.sh
[I][ Init][ 162]: LLM init start
[I][ Init][ 34]: connect http://127.0.0.1:12345 ok
[I][ Init][ 267]: IMAGE_CONTEXT_TOKEN: 151655, IMAGE_START_TOKEN: 151652
[I][ Init][ 328]: image encoder output float32
[I][ Init][ 340]: max_token_len : 1023
[I][ Init][ 343]: kv_cache_size : 256, kv_cache_num: 1023
[I][ Init][ 351]: prefill_token_num : 128
[I][ Init][ 355]: grp: 1, prefill_max_token_num : 1
[I][ Init][ 355]: grp: 2, prefill_max_token_num : 128
[I][ Init][ 355]: grp: 3, prefill_max_token_num : 256
[I][ Init][ 355]: grp: 4, prefill_max_token_num : 384
[I][ Init][ 355]: grp: 5, prefill_max_token_num : 512
[I][ Init][ 359]: prefill_max_token_num : 512
________________________
| ID| remain cmm(MB)|
========================
| 0| 2286|
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
[E][ load_config][ 278]: config file(post_config.json) open failed
[W][ Init][ 452]: load postprocess config(post_config.json) failed
[I][ Init][ 456]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
prompt >> 描述这张图片
image >> image/ssd_car.jpg
[I][ Encode][ 539]: image encode time : 772.851990 ms, size : 524288
[I][ Run][ 625]: input token num : 280, prefill_split_num : 3
[I][ Run][ 659]: input_num_token:128
[I][ Run][ 659]: input_num_token:128
[I][ Run][ 659]: input_num_token:24
[I][ Run][ 796]: ttft: 2067.18 ms
这张图片展示了一条繁忙的城市街道。前景中，一名女子站在人行道上，穿着黑色外套，面带微笑。她旁边是一辆红色的双层巴士，巴士上有一个广告，上面写着“THINGS GET MORE EXITING WHEN YOU SAY ‘YES’ VirginMoney.co.uk”。巴士的车牌号是“L15”。巴士旁边停着一辆黑色的面包车。背景中可以看到一些商店和行人，街道两旁有路灯和商店的招牌。整体环境显得非常繁忙和现代。
[N][ Run][ 949]: hit eos,avg 4.12 token/s
Video understand demo
Please pre-process the image of the video file into a 308x308 size picture
start tokenizer server for image understand demo
python qwen2_tokenizer_video_308.py --port 12345
run image understand demo
(base) axera@raspberrypi:~/lhj/Qwen2.5-VL-3B-Instruct $ bash run_qwen2_5_vl_video_axcl_aarch64.sh
[I][ Init][ 162]: LLM init start
[I][ Init][ 34]: connect http://127.0.0.1:12345 ok
[I][ Init][ 267]: IMAGE_CONTEXT_TOKEN: 151656, IMAGE_START_TOKEN: 151652
[I][ Init][ 328]: image encoder output float32
[I][ Init][ 340]: max_token_len : 1023
[I][ Init][ 343]: kv_cache_size : 256, kv_cache_num: 1023
[I][ Init][ 351]: prefill_token_num : 128
[I][ Init][ 355]: grp: 1, prefill_max_token_num : 1
[I][ Init][ 355]: grp: 2, prefill_max_token_num : 128
[I][ Init][ 355]: grp: 3, prefill_max_token_num : 256
[I][ Init][ 355]: grp: 4, prefill_max_token_num : 384
[I][ Init][ 355]: grp: 5, prefill_max_token_num : 512
[I][ Init][ 359]: prefill_max_token_num : 512
________________________
| ID| remain cmm(MB)|
========================
| 0| 2464|
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
[E][ load_config][ 278]: config file(post_config.json) open failed
[W][ Init][ 452]: load postprocess config(post_config.json) failed
[I][ Init][ 456]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
prompt >> 描述这个视频的内容
image >> video
video/frame_0000.jpg
video/frame_0008.jpg
video/frame_0016.jpg
video/frame_0024.jpg
video/frame_0032.jpg
video/frame_0040.jpg
video/frame_0048.jpg
video/frame_0056.jpg
[I][ Encode][ 539]: image encode time : 1481.107056 ms, size : 991232
[I][ Run][ 625]: input token num : 509, prefill_split_num : 4
[I][ Run][ 659]: input_num_token:128
[I][ Run][ 659]: input_num_token:128
[I][ Run][ 659]: input_num_token:128
[I][ Run][ 659]: input_num_token:125
[I][ Run][ 796]: ttft: 3049.59 ms
视频展示了两只松鼠在户外的场景。背景是模糊的山脉和蓝天，前景中有松鼠在互动。松鼠的毛色是棕色和灰色的混合，它们的爪子是橙色的。松鼠似乎在互相玩耍或争抢，它们的爪子和嘴巴都伸向对方。整个场景显得非常自然和生动。
[N][ Run][ 949]: hit eos,avg 4.15 token/s
- Downloads last month
- 17
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
🙋
Ask for provider support
Model tree for AXERA-TECH/Qwen2.5-VL-3B-Instruct
Base model
Qwen/Qwen2.5-VL-3B-Instruct