Qwen3-VL-8B-Instruct

AXERA-TECH/Qwen3-VL-8B-Instruct

Qwen3-VL-8B-Instruct is a image-text-to-text model from AXERA-TECH. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as mit.

This version of Qwen3-VL-2B-Instruct has been converted to run on the Axera NPU using w8a16 quantization.

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.axmodel10.1 GB · 89%

At a glance

Task: Image-Text-to-Text
Library: transformers
License: mit
Access: Public
Created: Nov 8, 2025
Updated: Nov 11, 2025
SHA: f3c9f23d

Try a prompt

Base models

Model card

From the Hugging Face model README

Qwen3-VL

This version of Qwen3-VL-2B-Instruct has been converted to run on the Axera NPU using w8a16 quantization.

Compatible with Pulsar2 version: 5.0

Convert tools links:

For those who are interested in model conversion, you can try to export axmodel through the original repo :

Pulsar2 Link, How to Convert LLM from Huggingface to axmodel

AXera NPU HOST LLM Runtime

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(168 tokens)	w8a16	CMM	Flash
AX650	384*384	1	280 ms	1476 ms	2.5 tokens/sec	11.8 GB	14 GB

Video Process

Chips	input size	image num	image encoder	ttft(600 tokens)	w8a16	CMM	Flash
AX650	384*384	8	1114 ms	4520 ms	2.5 tokens/sec	11.8 GB	14 GB

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

Prepare tokenizer server

Install transformer

pip install -r requirements.txt

Demo Run

Image understand demo

start tokenizer server for image understand demo

python3 tokenizer_images.py --port 8080

run image understand demo

input text

描述这张图片

input image

root@ax650 ~/Qwen3-VL-2B-Instruct-GPTQ-Int4 # bash run_image_ax650.sh 
[I][                            Init][ 156]: LLM init start
[I][                            Init][ 158]: Total CMM:4353 MB
[I][                            Init][  34]: connect http://127.0.0.1:8080 ok
bos_id: -1, eos_id: 151645
img_start_token: 151652
img_context_token: 151655
  3% | ██                                |   1 /  31 [0.01s<0.46s, 66.67 count/s] tokenizer init ok[I][                            Init][  26]: LLaMaEmbedSelector use mmap
  6% | ███                               |   2 /  31 [0.02s<0.34s, 90.91 count/s] embed_selector init ok[I][                            Init][ 201]: attr.axmodel_num:28
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
[I][                            Init][ 309]: image encoder output float32

[I][                            Init][ 339]: max_token_len : 2047
[I][                            Init][ 344]: kv_cache_size : 1024, kv_cache_num: 2047
[I][                            Init][ 352]: prefill_token_num : 128
[I][                            Init][ 356]: grp: 1, prefill_max_token_num : 1
[I][                            Init][ 356]: grp: 2, prefill_max_token_num : 128
[I][                            Init][ 356]: grp: 3, prefill_max_token_num : 256
[I][                            Init][ 356]: grp: 4, prefill_max_token_num : 384
[I][                            Init][ 356]: grp: 5, prefill_max_token_num : 512
[I][                            Init][ 356]: grp: 6, prefill_max_token_num : 640
[I][                            Init][ 356]: grp: 7, prefill_max_token_num : 768
[I][                            Init][ 356]: grp: 8, prefill_max_token_num : 896
[I][                            Init][ 356]: grp: 9, prefill_max_token_num : 1024
[I][                            Init][ 356]: grp: 10, prefill_max_token_num : 1152
[I][                            Init][ 360]: prefill_max_token_num : 1152
[I][                            Init][ 372]: LLM init ok
[I][                            Init][ 374]: Left CMM:854 MB
Type "q" to exit, Ctrl+c to stop current running
prompt >> 描述这张图片
image >> images/recoAll_attractions_1.jpg
[I][                     EncodeImage][ 440]: pixel_values size 1
[I][                     EncodeImage][ 441]: grid_h 24 grid_w 24
[I][                     EncodeImage][ 489]: image encode time : 237.778000 ms, size : 1
[I][                          Encode][ 532]: input_ids size:168
[I][                          Encode][ 540]: offset 15
[I][                          Encode][ 569]: img_embed.size:1, 294912
[I][                          Encode][ 583]: out_embed size:344064
[I][                          Encode][ 584]: input_ids size 168
[I][                          Encode][ 586]: position_ids size:168
[I][                             Run][ 607]: input token num : 168, prefill_split_num : 2
[I][                             Run][ 641]: input_num_token:128
[I][                             Run][ 641]: input_num_token:40
[I][                             Run][ 865]: ttft: 313.60 ms
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。

[N][                             Run][ 992]: hit eos,avg 14.14 token/s

Video understand demo

start tokenizer server for image understand demo

python tokenizer_video.py --port 8080

run video understand demo

input text

描述这个视频

input video

./video

root@ax650 ~/Qwen3-VL-2B-Instruct-GPTQ-Int4 # bash run_video_ax650.sh 
[I][                            Init][ 156]: LLM init start
[I][                            Init][ 158]: Total CMM:7884 MB
[I][                            Init][  34]: connect http://127.0.0.1:8080 ok
bos_id: -1, eos_id: 151645
img_start_token: 151652
img_context_token: 151656
  3% | ██                                |   1 /  31 [0.01s<0.34s, 90.91 count/s] tokenizer init ok[I][                            Init][  26]: LLaMaEmbedSelector use mmap
  6% | ███                               |   2 /  31 [0.01s<0.23s, 133.33 count/s] embed_selector init ok[I][                            Init][ 201]: attr.axmodel_num:28
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
[I][                            Init][ 309]: image encoder output float32

[I][                            Init][ 339]: max_token_len : 2047
[I][                            Init][ 344]: kv_cache_size : 1024, kv_cache_num: 2047
[I][                            Init][ 352]: prefill_token_num : 128
[I][                            Init][ 356]: grp: 1, prefill_max_token_num : 1
[I][                            Init][ 356]: grp: 2, prefill_max_token_num : 128
[I][                            Init][ 356]: grp: 3, prefill_max_token_num : 256
[I][                            Init][ 356]: grp: 4, prefill_max_token_num : 384
[I][                            Init][ 356]: grp: 5, prefill_max_token_num : 512
[I][                            Init][ 356]: grp: 6, prefill_max_token_num : 640
[I][                            Init][ 356]: grp: 7, prefill_max_token_num : 768
[I][                            Init][ 356]: grp: 8, prefill_max_token_num : 896
[I][                            Init][ 356]: grp: 9, prefill_max_token_num : 1024
[I][                            Init][ 356]: grp: 10, prefill_max_token_num : 1152
[I][                            Init][ 360]: prefill_max_token_num : 1152
[I][                            Init][ 372]: LLM init ok
[I][                            Init][ 374]: Left CMM:4385 MB
Type "q" to exit, Ctrl+c to stop current running
prompt >> 描述这个视频
video >> 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][                     EncodeImage][ 440]: pixel_values size 4
[I][                     EncodeImage][ 441]: grid_h 24 grid_w 24
[I][                     EncodeImage][ 489]: image encode time : 751.481018 ms, size : 4
[I][                          Encode][ 532]: input_ids size:600
[I][                          Encode][ 540]: offset 15
[I][                          Encode][ 569]: img_embed.size:4, 294912
[I][                          Encode][ 574]: offset:159
[I][                          Encode][ 574]: offset:303
[I][                          Encode][ 574]: offset:447
[I][                          Encode][ 583]: out_embed size:1228800
[I][                          Encode][ 584]: input_ids size 600
[I][                          Encode][ 586]: position_ids size:600
[I][                             Run][ 607]: input token num : 600, prefill_split_num : 5
[I][                             Run][ 641]: input_num_token:128
[I][                             Run][ 641]: input_num_token:128
[I][                             Run][ 641]: input_num_token:128
[I][                             Run][ 641]: input_num_token:128
[I][                             Run][ 641]: input_num_token:88
[I][                             Run][ 865]: ttft: 843.36 ms
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。

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。

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。

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。

[N][                             Run][ 992]: hit eos,avg 14.16 token/s

Task: Image-Text-to-Text
Library: transformers
License: mit
Languages: en, zh
Created: Nov 8, 2025
Updated: Nov 11, 2025