Qwen3-VL-2B-Instruct-GPTQ-Int4
This version of Qwen3-VL-2B-Instruct-GPTQ-Int4 has been converted to run on the WaveMatrix NPU using w4a16 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
Support Platform
- WM9955
- WM9955 DEMO Board
- M.2 Accelerator card
Image Process
| Chips | input size | image num | image encoder | ttft(168 tokens) | w4a16 | CMM | Flash |
|---|---|---|---|---|---|---|---|
| WM9955 | 384*384 | 1 | 238 ms | 323 ms | 12.1 tokens/sec | 2.5GiB | 3.3GiB |
Video Process
| Chips | input size | image num | image encoder | ttft(600 tokens) | w4a16 | CMM | Flash |
|---|---|---|---|---|---|---|---|
| WM9955 | 384*384 | 8 | 751 ms | 843 ms | 12.1 tokens/sec | 2.5GiB | 3.3GiB |
Image Process (Image Encoder U8+U16 Quantization)
| Chips | input size | image num | image encoder | ttft(168 tokens) | w4a16 | CMM | Flash |
|---|---|---|---|---|---|---|---|
| WM9955 | 384*384 | 1 | 135 ms | 323 ms | 12.1 tokens/sec | 2.5GiB | 3.3GiB |
Video Process (Image Encoder U8+U16 Quantization)
| Chips | input size | image num | image encoder | ttft(600 tokens) | w4a16 | CMM | Flash |
|---|---|---|---|---|---|---|---|
| WM9955 | 384*384 | 8 | 466 ms | 843 ms | 12.1 tokens/sec | 2.5GiB | 3.3GiB |
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
Install axllm
Approach 1: Clone the repository and run the install script
git clone -b axllm https://github.com/WaveMatrix/ax-llm.git
cd ax-llm
./install.sh
Approach 2: One line command installation (defaul branch axllm)
curl -fsSL https://raw.githubusercontent.com/WaveMatrix/ax-llm/axllm/install.sh | bash
Approach 3: Download the executable directly
Go to
https://github.com/WaveMatrix/ax-llm/actions?query=branch%3Aaxllm
Download the (axllm) then:
chmod +x axllm
sudo mv axllm /usr/bin/axllm
Download the model (Hugging Face)
Create the directory then get into it
mkdir -p WaveMatrix/Qwen3-VL-2B-Instruct-GPTQ-Int4
cd WaveMatrix/Qwen3-VL-2B-Instruct-GPTQ-Int4
hf download WaveMatrix/Qwen3-VL-2B-Instruct-GPTQ-Int4 --local-dir .
# structure of the downloaded files
tree -L 3
`-- WaveMatrix
`-- Qwen3-VL-2B-Instruct-GPTQ-Int4
|-- Qwen3-VL-2B-Instruct_vision.axmodel
|-- Qwen3-VL-2B-Instruct_vision_1280x736.axmodel
|-- Qwen3-VL-2B-Instruct_vision_640x640.axmodel
|-- Qwen3-VL-2B-Instruct_vision_u8.axmodel
|-- README.md
|-- config.json
|-- image.png
|-- model.embed_tokens.weight.bfloat16.bin
|-- post_config.json
|-- qwen3_tokenizer.txt
|-- qwen3_vl_text_p128_l0_together.axmodel
...
|-- qwen3_vl_text_p128_l9_together.axmodel
|-- qwen3_vl_text_post.axmodel
`-- vision_cache
3 directories, 39 files
Inference with WM9955 Host, such as WM9955 DEMO Board
Run via CLI
root@wm9955:~# axllm run WaveMatrix/Qwen3-VL-2B-Instruct-GPTQ-Int4/
[I][ Init][ 138]: LLM init start
tokenizer_type = 1
96% | ███████████████████████████████ | 30 / 31 [11.50s<11.88s, 2.61 count/s] init post axmodel ok,remain_cmm(9563 MB)
[I][ Init][ 199]: max_token_len : 2047
[I][ Init][ 202]: kv_cache_size : 1024, kv_cache_num: 2047
[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][ 209]: grp: 8, prefill_max_kv_cache_num : 896
[I][ Init][ 209]: grp: 9, prefill_max_kv_cache_num : 1024
[I][ Init][ 209]: grp: 10, prefill_max_kv_cache_num : 1152
[I][ Init][ 214]: prefill_max_token_num : 1152
[I][ Init][ 27]: LLaMaEmbedSelector use mmap
100% | ████████████████████████████████ | 31 / 31 [11.50s<11.50s, 2.70 count/s] embed_selector init ok
[W][ Init][ 457]: Qwen-VL vision size override: cfg=448x448 bytes=1204224, model_input_bytes=884736 -> 384x384 (square).
[I][ Init][ 641]: Qwen-VL token ids: vision_start=151652 image_pad=151655 video_pad=151656
[I][ Init][ 666]: VisionModule init ok: type=Qwen3VL, tokens_per_block=144, embed_size=2048, out_dtype=fp32
[I][ Init][ 672]: VisionModule deepstack enabled: layers=3
[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 >>
[I][ SetKVCache][ 406]: prefill_grpid:2 kv_cache_num:128 precompute_len:0 input_num_token:22
[I][ SetKVCache][ 408]: current prefill_max_token_num:1152
[I][ SetKVCache][ 409]: first run
[I][ Run][ 457]: input token num : 22, prefill_split_num : 1
[I][ Run][ 497]: prefill chunk p=0 history_len=0 grpid=1 kv_cache_num=0 input_tokens=22
[I][ Run][ 519]: prefill indices shape: p=0 idx_elems=384 idx_rows=3 pos_rows=0
[I][ Run][ 627]: ttft: 174.42 ms
I am Qwen, a large-scale language model developed by the Tongyi Lab of Alibaba Group. I can answer questions, write stories, create essays, and more. I am designed to be helpful, harmless, and honest. I hope to assist you in any way I can!
[N][ Run][ 709]: hit eos,avg 10.48 token/s
[I][ GetKVCache][ 380]: precompute_len:79, remaining:1073
prompt >> describe the image
image >> ./WaveMatrix/Qwen3-VL-2B-Instruct-WM9955-c128_p1152-int4/image.png
[I][ EncodeForContent][ 971]: Qwen-VL pixel_values[0] bytes=884736 min=0 max=241 (w=384 h=384 tp=2 ps=16 sm=2)
[I][ EncodeForContent][ 994]: vision cache store: ./WaveMatrix/Qwen3-VL-2B-Instruct-WM9955-c128_p1152-int4/image.png
[I][ SetKVCache][ 406]: prefill_grpid:3 kv_cache_num:256 precompute_len:79 input_num_token:159
[I][ SetKVCache][ 408]: current prefill_max_token_num:1024
[I][ Run][ 457]: input token num : 159, prefill_split_num : 2
[I][ Run][ 497]: prefill chunk p=0 history_len=79 grpid=2 kv_cache_num=128 input_tokens=128
[I][ Run][ 519]: prefill indices shape: p=0 idx_elems=384 idx_rows=3 pos_rows=3
[I][ Run][ 497]: prefill chunk p=1 history_len=207 grpid=3 kv_cache_num=256 input_tokens=31
[I][ Run][ 519]: prefill indices shape: p=1 idx_elems=384 idx_rows=3 pos_rows=3
[I][ Run][ 627]: ttft: 379.97 ms
This image depicts three astronauts in white space suits standing in a dense, leafy forest. The scene is set in a dark, shadowy environment, with the astronauts appearing to be in a natural, possibly alien, environment. The image has a monochromatic, almost grayscale color scheme, giving it a mysterious and somber atmosphere. The astronauts are positioned in the center of the frame, with one standing upright and the other two slightly bent, as if they are exploring or searching for something in the dense foliage. The overall mood of the image is mysterious and contemplative.
[N][ Run][ 709]: hit eos,avg 10.33 token/s
[I][ GetKVCache][ 380]: precompute_len:239, remaining:913
prompt >> how many people in the image?
image >>
[I][ EncodeForContent][ 926]: vision cache hit (mem): ./WaveMatrix/Qwen3-VL-2B-Instruct-WM9955-c128_p1152-int4/image.png
[I][ SetKVCache][ 406]: prefill_grpid:4 kv_cache_num:384 precompute_len:239 input_num_token:74
[I][ SetKVCache][ 408]: current prefill_max_token_num:896
[I][ Run][ 457]: input token num : 74, prefill_split_num : 1
[I][ Run][ 497]: prefill chunk p=0 history_len=239 grpid=3 kv_cache_num=256 input_tokens=74
[I][ Run][ 519]: prefill indices shape: p=0 idx_elems=384 idx_rows=3 pos_rows=3
[I][ Run][ 627]: ttft: 193.78 ms
This image depicts three astronauts in white space suits standing in a dense, leafy forest. The scene is set in a dark, shadowy environment, with the astronauts appearing to be in a natural, possibly alien, environment. The image has a monochromatic, almost grayscale color scheme, giving it a mysterious and somber atmosphere. The astronauts are positioned in the center of the frame, with one standing upright and the other two slightly bent, as if they are exploring or searching for something in the dense foliage. The overall mood of the image is mysterious and contemplative.
[N][ Run][ 709]: hit eos,avg 10.48 token/s
[I][ GetKVCache][ 380]: precompute_len:410, remaining:742
prompt >> q
Start the service (OpenAI compatible API)
root@wm9955:~# axllm serve WaveMatrix/Qwen3-VL-2B-Instruct-GPTQ-Int4
[I][ Init][ 138]: LLM init start
tokenizer_type = 1
96% | ███████████████████████████████ | 30 / 31 [4.63s<4.79s, 6.47 count/s] init post axmodel ok,remain_cmm(9563 MB)
[I][ Init][ 199]: max_token_len : 2047
[I][ Init][ 202]: kv_cache_size : 1024, kv_cache_num: 2047
[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][ 209]: grp: 8, prefill_max_kv_cache_num : 896
[I][ Init][ 209]: grp: 9, prefill_max_kv_cache_num : 1024
[I][ Init][ 209]: grp: 10, prefill_max_kv_cache_num : 1152
[I][ Init][ 214]: prefill_max_token_num : 1152
[I][ Init][ 27]: LLaMaEmbedSelector use mmap
100% | ████████████████████████████████ | 31 / 31 [4.64s<4.64s, 6.69 count/s] embed_selector init ok
[W][ Init][ 457]: Qwen-VL vision size override: cfg=448x448 bytes=1204224, model_input_bytes=884736 -> 384x384 (square).
[I][ Init][ 641]: Qwen-VL token ids: vision_start=151652 image_pad=151655 video_pad=151656
[I][ Init][ 666]: VisionModule init ok: type=Qwen3VL, tokens_per_block=144, embed_size=2048, out_dtype=fp32
[I][ Init][ 672]: VisionModule deepstack enabled: layers=3
[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 'WaveMatrix/Qwen3-VL-2B-Instruct-GPTQ-Int4'...
OpenAI API Server starting on http://0.0.0.0:8000
Max concurrency: 1
Models: WaveMatrix/Qwen3-VL-2B-Instruct-GPTQ-Int4
OpenAI API call example
from openai import OpenAI
API_URL = "http://127.0.0.1:8000/v1"
MODEL = "WaveMatrix/Qwen3-VL-2B-Instruct-GPTQ-Int4"
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 streaming API call example
from openai import OpenAI
API_URL = "http://127.0.0.1:8000/v1"
MODEL = "WaveMatrix/Qwen3-VL-2B-Instruct-GPTQ-Int4"
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("
")
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