SmolVLM2-500M-Video-Instruct

SmolVLM2-500M-Video-Instruct

This version of SmolVLM2-500M-Video-Instructhas 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 :

• https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-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

Video Process

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

安装 axllm

方式一：克隆仓库后执行安装脚本：

git clone -b axllm https://github.com/AXERA-TECH/ax-llm.git
cd ax-llm
./install.sh

方式二：一行命令安装（默认分支 axllm）：

curl -fsSL https://raw.githubusercontent.com/AXERA-TECH/ax-llm/axllm/install.sh | bash

方式三：下载Github Actions CI 导出的可执行程序（适合没有编译环境的用户）：

如果没有编译环境，请到： https://github.com/AXERA-TECH/ax-llm/actions?query=branch%3Aaxllm 下载 最新 CI 导出的可执行程序（axllm），然后：

chmod +x axllm
sudo mv axllm /usr/bin/axllm

模型下载（Hugging Face）

先创建模型目录并进入，然后下载到该目录：

mkdir -p AXERA-TECH/SmolVLM2-500M-Video-Instruct
cd AXERA-TECH/SmolVLM2-500M-Video-Instruct
hf download AXERA-TECH/SmolVLM2-500M-Video-Instruct --local-dir .

# structure of the downloaded files
tree -L 3
.
└── AXERA-TECH
    └── SmolVLM2-500M-Video-Instruct
        ├── config.json
        ├── image.png
        ├── llama_p128_l0_together.axmodel
        ...
        ├── llama_p128_l9_together.axmodel
        ├── llama_post.axmodel
        ├── model.embed_tokens.weight.bfloat16.bin
        ├── post_config.json
        ├── README.md
        ├── smolvlm2_tokenizer.txt
        ├── vision_cache
        └── vision_model_1x3x512x512_NHwC_U8.axmodel

4 directories, 40 files

Inference with AX650 Host, such as M4N-Dock(爱芯派Pro) or AX650N DEMO Board

运行（CLI）

root@ax650:~# axllm run AXERA-TECH/SmolVLM2-500M-Video-Instruct/
[I][                            Init][ 138]: LLM init start
tokenizer_type = 1
 97% | ████████████████████████████████  |  34 /  35 [3.08s<3.17s, 11.03 count/s] init post axmodel ok,remain_cmm(11158 MB)
[I][                            Init][ 199]: max_token_len : 1023
[I][                            Init][ 202]: kv_cache_size : 320, kv_cache_num: 1023
[I][                            Init][ 205]: prefill_token_num : 128
[I][                            Init][ 209]: grp: 1, prefill_max_kv_cache_num : 1
[I][                            Init][ 209]: grp: 2, prefill_max_kv_cache_num : 128
[I][                            Init][ 209]: grp: 3, prefill_max_kv_cache_num : 256
[I][                            Init][ 209]: grp: 4, prefill_max_kv_cache_num : 384
[I][                            Init][ 209]: grp: 5, prefill_max_kv_cache_num : 512
[I][                            Init][ 209]: grp: 6, prefill_max_kv_cache_num : 640
[I][                            Init][ 209]: grp: 7, prefill_max_kv_cache_num : 768
[I][                            Init][ 214]: prefill_max_token_num : 768
[I][                            Init][  27]: LLaMaEmbedSelector use mmap
100% | ████████████████████████████████ |  35 /  35 [3.08s<3.08s, 11.35 count/s] embed_selector init ok
[W][                            Init][ 526]: SmolVLM2 vision size override: cfg=448x448 -> model=512x512
[I][                            Init][ 666]: VisionModule init ok: type=SmolVLM2, tokens_per_block=64, embed_size=960, out_dtype=fp32
[I][                     load_config][ 282]: load config:
{
    "enable_repetition_penalty": false,
    "enable_temperature": false,
    "enable_top_k_sampling": false,
    "enable_top_p_sampling": false,
    "penalty_window": 20,
    "repetition_penalty": 1.2,
    "temperature": 0.9,
    "top_k": 10,
    "top_p": 0.8
}

[I][                            Init][ 272]: LLM init ok
Type "q" to exit
Ctrl+c to stop current running
"reset" to reset kvcache
"dd" to remove last conversation.
"pp" to print history.
VLM enabled: after each prompt, input image path (empty = text-only). Use "video:<frames_dir>" for video.
----------------------------------------
prompt >> who are you
image >>
[W][             apply_chat_template][  80]: system content is not supported
[I][                      SetKVCache][ 406]: prefill_grpid:2 kv_cache_num:128 precompute_len:0 input_num_token:11
[I][                      SetKVCache][ 408]: current prefill_max_token_num:768
[I][                      SetKVCache][ 409]: first run
[I][                             Run][ 457]: input token num : 11, prefill_split_num : 1
[I][                             Run][ 497]: prefill chunk p=0 history_len=0 grpid=1 kv_cache_num=0 input_tokens=11
[I][                             Run][ 519]: prefill indices shape: p=0 idx_elems=128 idx_rows=1 pos_rows=0
[I][                             Run][ 627]: ttft: 119.38 ms
 I'm an AI assistant, I don't have personal experiences or emotions, but I can provide information and answer questions based on my programming and knowledge base.

I was trained on a vast amount of text data, including books, articles, and websites, which enables me to understand and respond to a wide range of questions.

If you have a question or need help with a task, feel free to ask!

[N][                             Run][ 709]: hit eos,avg 29.32 token/s

[I][                      GetKVCache][ 380]: precompute_len:96, remaining:672
[W][             apply_chat_template][  80]: system content is not supported
prompt >> how many people in the image?
image >> ./AXERA-TECH/SmolVLM2-500M-Video-Instruct/image.png
[I][                EncodeForContent][ 994]: vision cache store: ./AXERA-TECH/SmolVLM2-500M-Video-Instruct/image.png
[W][             apply_chat_template][  80]: system content is not supported
[I][                      SetKVCache][ 406]: prefill_grpid:5 kv_cache_num:512 precompute_len:96 input_num_token:350
[I][                      SetKVCache][ 408]: current prefill_max_token_num:640
[I][                             Run][ 457]: input token num : 350, prefill_split_num : 3
[I][                             Run][ 497]: prefill chunk p=0 history_len=96 grpid=2 kv_cache_num=128 input_tokens=128
[I][                             Run][ 519]: prefill indices shape: p=0 idx_elems=128 idx_rows=1 pos_rows=0
[I][                             Run][ 497]: prefill chunk p=1 history_len=224 grpid=3 kv_cache_num=256 input_tokens=128
[I][                             Run][ 519]: prefill indices shape: p=1 idx_elems=128 idx_rows=1 pos_rows=0
[I][                             Run][ 497]: prefill chunk p=2 history_len=352 grpid=4 kv_cache_num=384 input_tokens=94
[I][                             Run][ 519]: prefill indices shape: p=2 idx_elems=128 idx_rows=1 pos_rows=0
[I][                             Run][ 627]: ttft: 681.38 ms
 There are three people in the image.

[N][                             Run][ 709]: hit eos,avg 30.19 token/s

[I][                      GetKVCache][ 380]: precompute_len:454, remaining:314
[W][             apply_chat_template][  80]: system content is not supported
prompt >> q

启动服务（OpenAI 兼容）

root@ax650:~# axllm serve AXERA-TECH/SmolVLM2-500M-Video-Instruct/
[I][                            Init][ 138]: LLM init start
tokenizer_type = 1
 97% | ████████████████████████████████  |  34 /  35 [1.91s<1.97s, 17.77 count/s] init post axmodel ok,remain_cmm(11158 MB)
[I][                            Init][ 199]: max_token_len : 1023
[I][                            Init][ 202]: kv_cache_size : 320, kv_cache_num: 1023
[I][                            Init][ 205]: prefill_token_num : 128
[I][                            Init][ 209]: grp: 1, prefill_max_kv_cache_num : 1
[I][                            Init][ 209]: grp: 2, prefill_max_kv_cache_num : 128
[I][                            Init][ 209]: grp: 3, prefill_max_kv_cache_num : 256
[I][                            Init][ 209]: grp: 4, prefill_max_kv_cache_num : 384
[I][                            Init][ 209]: grp: 5, prefill_max_kv_cache_num : 512
[I][                            Init][ 209]: grp: 6, prefill_max_kv_cache_num : 640
[I][                            Init][ 209]: grp: 7, prefill_max_kv_cache_num : 768
[I][                            Init][ 214]: prefill_max_token_num : 768
[I][                            Init][  27]: LLaMaEmbedSelector use mmap
100% | ████████████████████████████████ |  35 /  35 [1.91s<1.91s, 18.29 count/s] embed_selector init ok
[W][                            Init][ 526]: SmolVLM2 vision size override: cfg=448x448 -> model=512x512
[I][                            Init][ 666]: VisionModule init ok: type=SmolVLM2, tokens_per_block=64, embed_size=960, out_dtype=fp32
[I][                     load_config][ 282]: load config:
{
    "enable_repetition_penalty": false,
    "enable_temperature": false,
    "enable_top_k_sampling": false,
    "enable_top_p_sampling": false,
    "penalty_window": 20,
    "repetition_penalty": 1.2,
    "temperature": 0.9,
    "top_k": 10,
    "top_p": 0.8
}

[I][                            Init][ 272]: LLM init ok
Starting server on port 8000 with model 'AXERA-TECH/SmolVLM2-500M-Video-Instruct'...
OpenAI API Server starting on http://0.0.0.0:8000
Max concurrency: 1
Models: AXERA-TECH/SmolVLM2-500M-Video-Instruct

OpenAI 调用示例

from openai import OpenAI

API_URL = "http://127.0.0.1:8000/v1"
MODEL = "AXERA-TECH/SmolVLM2-500M-Video-Instruct"

messages = [
    {"role": "system", "content": [{"type": "text", "text": "you are a helpful assistant."}]},
    {"role": "user", "content": "hello"},
]

client = OpenAI(api_key="not-needed", base_url=API_URL)
completion = client.chat.completions.create(
    model=MODEL,
    messages=messages,
)

print(completion.choices[0].message.content)

OpenAI 流式调用示例

from openai import OpenAI

API_URL = "http://127.0.0.1:8000/v1"
MODEL = "AXERA-TECH/SmolVLM2-500M-Video-Instruct"

messages = [
    {"role": "system", "content": [{"type": "text", "text": "you are a helpful assistant."}]},
    {"role": "user", "content": "hello"},
]

client = OpenAI(api_key="not-needed", base_url=API_URL)
stream = client.chat.completions.create(
    model=MODEL,
    messages=messages,
    stream=True,
)

print("assistant:")
for ev in stream:
    delta = getattr(ev.choices[0], "delta", None)
    if delta and getattr(delta, "content", None):
        print(delta.content, end="", flush=True)
print("
")