README.md

---
license: mit
language:
  - en
  - zh
base_model:
  - OpenGVLab/InternVL3-2B
pipeline_tag: visual-question-answering
tags:
  - OpenGVLab
  - InternVL3-2B
---

# InternVL3-2B

This version of InternVL3-2B 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: 4.2

## Convert tools links:

For those who are interested in model conversion, you can try to export axmodel through the original repo : 
https://huggingface.co/OpenGVLab/InternVL3-2B

[How to Convert LLM from Huggingface to axmodel](https://github.com/AXERA-TECH/InternVL3-2B.axera/tree/master/model_convert) 

[AXera NPU HOST LLM Runtime](https://github.com/AXERA-TECH/ax-llm/tree/ax-internvl) 

[AXera NPU AXCL LLM Runtime](https://github.com/AXERA-TECH/ax-llm/tree/axcl-internvl)

## Support Platform

- AX650
  - AX650N DEMO Board
  - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
  - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)

|chips|Image num|image encoder 448 | ttft | w8a16 |
|--|--|--|--|--|
|AX650N | 0 | 0 ms | 221 ms (128 tokens) | 10 tokens/sec |
|AX650N | 1 | 364 ms | 862 ms (384 tokens) | 10 tokens/sec |
|AX650N | 4 | 1456 ms | 4589 ms (1152 tokens) | 10 tokens/sec |
|AX650N | 8 | 2912 ms | 13904 ms (2176 tokens) | 10 tokens/sec |

## How to use

Download all files from this repository to the device.

```bash
root@ax650:~/huggingface/InternVL3-2B# tree -L 1
.
|-- README.md
|-- config.json
|-- examples
|-- gradio_demo.py
|-- gradio_demo_c_api.py
|-- gradio_demo_python_api.py
|-- infer.py
|-- infer_video.py
|-- internvl3_2b_axmodel
|-- internvl3_2b_tokenizer
|-- internvl3_tokenizer.py
|-- llm.py
|-- main_api_ax650
|-- main_api_axcl_aarch64
|-- main_api_axcl_x86
|-- main_ax650
|-- main_axcl_aarch64
|-- main_axcl_x86
|-- post_config.json
|-- requirements.txt
|-- run_internvl_3_2b_448_api_ax650.sh
|-- run_internvl_3_2b_448_api_axcl_aarch64.sh
|-- run_internvl_3_2b_448_api_axcl_x86.sh
|-- run_internvl_3_2b_448_ax650.sh
|-- run_internvl_3_2b_448_axcl_aarch64.sh
|-- run_internvl_3_2b_448_axcl_x86.sh
|-- vit_axmodel
`-- webgui.png

4 directories, 24 files
```

### python env requirement

#### pyaxengine

https://github.com/AXERA-TECH/pyaxengine

```
wget https://github.com/AXERA-TECH/pyaxengine/releases/download/0.1.3.rc1/axengine-0.1.3-py3-none-any.whl
pip install axengine-0.1.3-py3-none-any.whl
```

#### others

```
pip install -r requirements.txt
```

#### Inference with Raspberry Pi 5 Host using AXCL EP(such as M.2 AI Card or HAT AI Module)

```
cd InternVL3-2B
python gradio_demo_python_api.py --hf_model internvl3_2b_tokenizer/ \
                                 --axmodel_path internvl3_2b_axmodel/ \
                                 --vit_model vit_axmodel/internvl3_2b_vit_slim.axmodel

[INFO] Available providers:  ['AXCLRTExecutionProvider']
Init InferenceSession:   0%|                                                                                 | 0/28 [00:00<?, ?it/s]
[INFO] Using provider: AXCLRTExecutionProvider
[INFO] SOC Name: AX650N
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Compiler version: 3.4 162fdaa8
Init InferenceSession:   4%|███▏                                                                             | 1/28 [00:01<00:43,  1.61s/it]
[INFO] Using provider: AXCLRTExecutionProvider
......
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Compiler version: 3.4 162fdaa8
Init InferenceSession: 100%|█████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:34<00:00,  1.23s/it]
[INFO] Using provider: AXCLRTExecutionProvider
[INFO] SOC Name: AX650N
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Compiler version: 3.4 162fdaa8
model load done!
[INFO] Using provider: AXCLRTExecutionProvider
[INFO] SOC Name: AX650N
[INFO] VNPU type: VNPUType.DISABLED
[INFO] Compiler version: 3.4 162fdaa8
  chatbot = gr.Chatbot(height=650)
HTTP 服务地址: http://xxx.xxx.xxx.xxx:7860
* Running on local URL:  http://xxx.xxx.xxx.xxx:7860
* To create a public link, set `share=True` in `launch()`.
```

Access `http://xxx.xxx.xxx.xxx:7860` using Chrome or another browser.

![](webgui.png)

#### Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650N DEMO Board(C++ sample)
#### Start the Tokenizer service

```
root@ax650:~/huggingface/InternVL3-2B# python3 internvl3_tokenizer.py
None None 151645 <|im_end|> 151665 151667
context_len is  256
prompt is <|im_start|>system
你是书生·万象, 英文名是InternVL, 是由上海人工智能实验室、清华大学及多家合作单位联合开发的多模态大语言模型.<|im_end|
...
http://0.0.0.0:12345

```
Open another terminal and run `run_internvl_3_2b_448_ax650.sh`
```
root@ax650:~/wangli/huggingface/InternVL3-2B# ./run_internvl_3_2b_448_ax650.sh
[I][                            Init][ 134]: LLM init start
[I][                            Init][  34]: connect http://0.0.0.0:12345 ok
bos_id: -1, eos_id: 151645
img_start_token: 151665
img_context_token: 151667
  3% | ██                                |   1 /  31 [0.01s<0.37s, 83.33 count/s] tokenizer init ok[I][                            Init][  45]: LLaMaEmbedSelector use mmap
  6% | ███                               |   2 /  31 [0.01s<0.19s, 166.67 count/s] embed_selector init ok
100% | ████████████████████████████████ |  31 /  31 [6.26s<6.26s, 4.95 count/s] init post axmodel ok,remain_cmm(7416 MB)[I][                            Init][ 226]: IMAGE_CONTEXT_TOKEN: 151667, IMAGE_START_TOKEN: 151665
[I][                            Init][ 251]: image encoder input nchw@float32
[I][                            Init][ 281]: image encoder output float32

[I][                            Init][ 291]: image_encoder_height : 448, image_encoder_width: 448
[I][                            Init][ 293]: max_token_len : 2559
[I][                            Init][ 296]: kv_cache_size : 256, kv_cache_num: 2559
[I][                            Init][ 304]: prefill_token_num : 128
[I][                            Init][ 308]: grp: 1, prefill_max_token_num : 1
[I][                            Init][ 308]: grp: 2, prefill_max_token_num : 128
[I][                            Init][ 308]: grp: 3, prefill_max_token_num : 256
[I][                            Init][ 308]: grp: 4, prefill_max_token_num : 384
[I][                            Init][ 308]: grp: 5, prefill_max_token_num : 512
[I][                            Init][ 308]: grp: 6, prefill_max_token_num : 640
[I][                            Init][ 308]: grp: 7, prefill_max_token_num : 768
[I][                            Init][ 308]: grp: 8, prefill_max_token_num : 896
[I][                            Init][ 308]: grp: 9, prefill_max_token_num : 1024
[I][                            Init][ 312]: prefill_max_token_num : 1024
[I][                     load_config][ 282]: load config:
{
    "enable_repetition_penalty": false,
    "enable_temperature": true,
    "enable_top_k_sampling": true,
    "enable_top_p_sampling": false,
    "penalty_window": 20,
    "repetition_penalty": 1.2,
    "temperature": 0.9,
    "top_k": 10,
    "top_p": 0.8
}

[I][                            Init][ 321]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
prompt >> 你是谁
image >>
[I][                             Run][ 551]: input token num : 46, prefill_split_num : 1
[I][                             Run][ 566]: prefill grpid 2
[I][                             Run][ 593]: input_num_token:46
[I][                             Run][ 717]: ttft: 311.26 ms
你好！我是商汤科技开发的多模态大模型，英文名叫InternVL。很高兴为你服务！请问有什么可以帮助你的吗？

[N][                             Run][ 826]: hit eos,avg 10.69 token/s

prompt >> 描述一下这张图片
image >> examples/image_0.jpg
[I][                          Encode][ 415]: image encode time : 408.81 ms, size : 393216
[I][                          Encode][ 524]: idx:0 offset : 49 out_embed.size() : 477696
[I][                             Run][ 551]: input token num : 311, prefill_split_num : 3
[I][                             Run][ 566]: prefill grpid 4
[I][                             Run][ 593]: input_num_token:128
[I][                             Run][ 593]: input_num_token:128
[I][                             Run][ 593]: input_num_token:55
[I][                             Run][ 717]: ttft: 1325.82 ms
这张图片展示了一只可爱的红熊猫。红熊猫是一种生活在亚洲森林中的熊科动物，以其红棕色的毛皮和白脸而闻名。图片中的红熊猫正趴在木板上，身体的一部分探出木板，显得有些放松和好奇。它的眼睛圆圆的，黑色的，看起来非常可爱。毛皮主要是棕红色的，耳朵和腹部是白色的，形成了鲜明的对比。背景中可以看到一些树木和绿色的叶子，暗示这可能是在自然的森林环境中拍摄的。整体上，这张图片传达出一种温暖和亲近自然的感觉。

[N][                             Run][ 826]: hit eos,avg 10.70 token/s

prompt >> q
```