Qwen3
Collection
3 items
β’
Updated
Qwen3-4B-Int8
This version of Qwen3-4B-Int8 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: 4.0-temp(Not released yet)
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/Qwen3-4B
Pulsar2 Link, How to Convert LLM from Huggingface to axmodel
Support Platform
| Chips | w8a16 | w4a16 |
|---|---|---|
| AX650 | 4.5 tokens/sec | TBD |
How to use
Download all files from this repository to the device
root@ax650:/mnt/qtang/llm-test/qwen3-4b# tree -L 1
.
|-- config.json
|-- main_ax650
|-- main_axcl_aarch64
|-- main_axcl_x86
|-- post_config.json
|-- qwen2.5_tokenizer
|-- qwen3-4b-ax650
|-- qwen3_tokenizer
|-- qwen3_tokenizer_uid.py
|-- run_qwen3_4b_int8_ctx_ax650.sh
|-- run_qwen3_4b_int8_ctx_axcl_aarch64.sh
`-- run_qwen3_4b_int8_ctx_axcl_x86.sh
3 directories, 9 files
root@ax650:/mnt/qtang/llm-test/qwen3-4b#
Start the Tokenizer service
Install requirement
pip install transformers jinja2
root@ax650:/mnt/qtang/llm-test/qwen3-4b# python3 qwen3_tokenizer_uid.py
None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.
Server running at http://0.0.0.0:12345
Inference with AX650 Host, such as M4N-Dock(η±θ―ζ΄ΎPro) or AX650N DEMO Board
Open another terminal and run run_qwen3_4b_int8_ctx_ax650.sh
root@ax650:/mnt/qtang/llm-test/qwen3-4b# ./run_qwen3_4b_int8_ctx_ax650.sh
[I][ Init][ 110]: LLM init start
[I][ Init][ 34]: connect http://127.0.0.1:12345 ok
[I][ Init][ 57]: uid: 6e90ff82-b9c9-42dc-8f61-081203389166
bos_id: -1, eos_id: 151645
2% | β | 1 / 39 [3.95s<153.89s, 0.25 count/s] tokenizer init ok
[I][ Init][ 26]: LLaMaEmbedSelector use mmap
100% | ββββββββββββββββββββββββββββββββ | 39 / 39 [48.03s<48.03s, 0.81 count/s] init post axmodel ok,remain_cmm(5621 MB)
[I][ Init][ 188]: max_token_len : 2559
[I][ Init][ 193]: kv_cache_size : 1024, kv_cache_num: 2559
[I][ Init][ 201]: prefill_token_num : 128
[I][ Init][ 205]: grp: 1, prefill_max_token_num : 1
[I][ Init][ 205]: grp: 2, prefill_max_token_num : 256
[I][ Init][ 205]: grp: 3, prefill_max_token_num : 512
[I][ Init][ 205]: grp: 4, prefill_max_token_num : 1024
[I][ Init][ 205]: grp: 5, prefill_max_token_num : 1536
[I][ Init][ 205]: grp: 6, prefill_max_token_num : 2048
[I][ Init][ 209]: prefill_max_token_num : 2048
[I][ load_config][ 282]: load config:
{
"enable_repetition_penalty": false,
"enable_temperature": false,
"enable_top_k_sampling": true,
"enable_top_p_sampling": false,
"penalty_window": 20,
"repetition_penalty": 1.2,
"temperature": 0.9,
"top_k": 1,
"top_p": 0.8
}
[I][ Init][ 218]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
[I][ GenerateKVCachePrefill][ 270]: input token num : 21, prefill_split_num : 1 prefill_grpid : 2
[I][ GenerateKVCachePrefill][ 307]: input_num_token:21
[I][ main][ 230]: precompute_len: 21
[I][ main][ 231]: system_prompt: You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
prompt >> 1+3=?
[I][ SetKVCache][ 530]: prefill_grpid:2 kv_cache_num:256 precompute_len:21 input_num_token:16
[I][ SetKVCache][ 533]: current prefill_max_token_num:1920
[I][ Run][ 659]: input token num : 16, prefill_split_num : 1
[I][ Run][ 685]: input_num_token:16
[I][ Run][ 808]: ttft: 1169.05 ms
<think>
</think>
1 + 3 = 4
[N][ Run][ 922]: hit eos,avg 4.22 token/s
[I][ GetKVCache][ 499]: precompute_len:48, remaining:2000
prompt >> who are you?
[I][ SetKVCache][ 530]: prefill_grpid:2 kv_cache_num:256 precompute_len:48 input_num_token:16
[I][ SetKVCache][ 533]: current prefill_max_token_num:1920
[I][ Run][ 659]: input token num : 16, prefill_split_num : 1
[I][ Run][ 685]: input_num_token:16
[I][ Run][ 808]: ttft: 1168.56 ms
<think>
</think>
I am Qwen, a large-scale language model developed by Alibaba Cloud. I can answer questions, create content,
and help with a variety of tasks. How can I assist you today?
[N][ Run][ 922]: hit eos,avg 4.22 token/s
[I][ GetKVCache][ 499]: precompute_len:106, remaining:1942
prompt >> q
root@ax650:/mnt/qtang/llm-test/qwen3-4b#
Inference with M.2 Accelerator card
What is M.2 Accelerator card?, Show this DEMO based on Raspberry PI 5.
(base) axera@raspberrypi:~/samples/qwen3-4b $ ./run_qwen3_4b_int8_ctx_axcl_aarch64.sh
[I][ Init][ 136]: LLM init start
[I][ Init][ 34]: connect http://127.0.0.1:12345 ok
[I][ Init][ 57]: uid: a5b1e427-0cdf-4da6-b3a7-f5e0517da0bb
bos_id: -1, eos_id: 151645
2% | β | 1 / 39 [0.99s<38.45s, 1.01 count/s] tokenizer init ok
[I][ Init][ 45]: LLaMaEmbedSelector use mmap
5% | ββ | 2 / 39 [0.99s<19.23s, 2.03 count/s] embed_selector init ok
[I][ run][ 30]: AXCLWorker start with devid 0
100% | ββββββββββββββββββββββββββββββββ | 39 / 39 [133.16s<133.16s, 0.29 count/s] init post axmodel ok,remain_cmm(691 MB)(1096 MB)000000000
[I][ Init][ 237]: max_token_len : 2559
[I][ Init][ 240]: kv_cache_size : 1024, kv_cache_num: 2559
[I][ Init][ 248]: prefill_token_num : 128
[I][ Init][ 252]: grp: 1, prefill_max_token_num : 1
[I][ Init][ 252]: grp: 2, prefill_max_token_num : 256
[I][ Init][ 252]: grp: 3, prefill_max_token_num : 512
[I][ Init][ 252]: grp: 4, prefill_max_token_num : 1024
[I][ Init][ 252]: grp: 5, prefill_max_token_num : 1536
[I][ Init][ 252]: grp: 6, prefill_max_token_num : 2048
[I][ Init][ 256]: prefill_max_token_num : 2048
________________________
| ID| remain cmm(MB)|
========================
| 0| 691|
Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―Β―
[I][ load_config][ 282]: load config:
{
"enable_repetition_penalty": false,
"enable_temperature": false,
"enable_top_k_sampling": true,
"enable_top_p_sampling": false,
"penalty_window": 20,
"repetition_penalty": 1.2,
"temperature": 0.9,
"top_k": 1,
"top_p": 0.8
}
[I][ Init][ 279]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
[I][ GenerateKVCachePrefill][ 335]: input token num : 21, prefill_split_num : 1 prefill_grpid : 2
[I][ GenerateKVCachePrefill][ 372]: input_num_token:21
[I][ main][ 236]: precompute_len: 21
[I][ main][ 237]: system_prompt: You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
prompt >> who are you
[I][ SetKVCache][ 628]: prefill_grpid:2 kv_cache_num:256 precompute_len:21 input_num_token:27
[I][ SetKVCache][ 631]: current prefill_max_token_num:1920
[I][ Run][ 869]: input token num : 27, prefill_split_num : 1
[I][ Run][ 901]: input_num_token:27
[I][ Run][1030]: ttft: 1339.01 ms
<think>
</think>
I am Qwen, a large-scale language model developed by Alibaba Cloud. I can answer questions,
create content, and help with a variety of tasks. What can I assist you with?
[N][ Run][1182]: hit eos,avg 3.65 token/s
[I][ GetKVCache][ 597]: precompute_len:90, remaining:1958
prompt >> q
[I][ run][ 80]: AXCLWorker exit with devid 0
(base) axera@raspberrypi:~/samples/qwen3-4b $
(base) axera@raspberrypi:~ $ axcl-smi
+------------------------------------------------------------------------------------------------+
| AXCL-SMI V3.4.0_20250423020139 Driver V3.4.0_20250423020139 |
+-----------------------------------------+--------------+---------------------------------------+
| Card Name Firmware | Bus-Id | Memory-Usage |
| Fan Temp Pwr:Usage/Cap | CPU NPU | CMM-Usage |
|=========================================+==============+=======================================|
| 0 AX650N V3.4.0 | 0000:01:00.0 | 193 MiB / 945 MiB |
| -- 37C -- / -- | 2% 0% | 6348 MiB / 7040 MiB |
+-----------------------------------------+--------------+---------------------------------------+
+------------------------------------------------------------------------------------------------+
| Processes: |
| Card PID Process Name NPU Memory Usage |
|================================================================================================|
| 0 84643 /home/axera/samples/qwen3-4b/main_axcl_aarch64 4894032 KiB |
+------------------------------------------------------------------------------------------------+
(base) axera@raspberrypi:~ $
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