git clone https://huggingface.co/AXERA-TECH/Qwen3-0.6B
File Description
m5stack@raspberrypi:~/Qwen3-0.6B $ ls -lh
total 4.4M
-rw-r--r-- 1 m5stack m5stack 0 Jul 25 15:48 config.json
-rw-r--r-- 1 m5stack m5stack 959K Jul 25 15:54 main_ax650
-rw-r--r-- 1 m5stack m5stack 1.7M Jul 25 15:54 main_axcl_aarch64
-rw-r--r-- 1 m5stack m5stack 1.8M Jul 25 15:54 main_axcl_x86
-rw-r--r-- 1 m5stack m5stack 277 Jul 25 15:47 post_config.json
drwxr-xr-x 2 m5stack m5stack 4.0K Jul 25 15:47 qwen2.5_tokenizer
drwxr-xr-x 2 m5stack m5stack 4.0K Jul 25 15:47 qwen3-0.6b-ax630c
drwxr-xr-x 2 m5stack m5stack 4.0K Jul 25 15:47 qwen3-0.6b-ax650
drwxr-xr-x 2 m5stack m5stack 4.0K Jul 25 15:47 qwen3_tokenizer
-rw-r--r-- 1 m5stack m5stack 7.6K Jul 25 15:47 qwen3_tokenizer_uid.py
-rw-r--r-- 1 m5stack m5stack 11K Jul 25 15:47 README.md
-rw-r--r-- 1 m5stack m5stack 577 Jul 25 15:47 run_qwen3_0.6b_int8_ctx_ax630c.sh
-rw-r--r-- 1 m5stack m5stack 574 Jul 25 15:47 run_qwen3_0.6b_int8_ctx_ax650.sh
-rw-r--r-- 1 m5stack m5stack 594 Jul 25 15:47 run_qwen3_0.6b_int8_ctx_axcl_aarch64.sh
-rw-r--r-- 1 m5stack m5stack 590 Jul 25 15:47 run_qwen3_0.6b_int8_ctx_axcl_x86.sh
python -m venv qwen
source qwen/bin/activate
pip install transformers jinja2
python qwen3_tokenizer_uid.py --port 12345
(qwen) m5stack@raspberrypi:~/Qwen3-0.6B $ python qwen3_tokenizer_uid.py --port 12345
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
chmod +x main_axcl_aarch64 run_qwen3_0.6b_int8_ctx_axcl_aarch64.sh
./run_qwen3_0.6b_int8_ctx_axcl_aarch64.sh
Once started successfully, the information will be as follows:
m5stack@raspberrypi:~/rsp/Qwen3-0.6B$ ./run_qwen3_0.6b_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: abf93a3d-2d6a-4ddb-8c9b-42a208a012f7
bos_id: -1, eos_id: 151645
3% | ██ | 1 / 31 [1.11s<34.47s, 0.90 count/s] tokenizer init ok[I][Init][ 45]: LLaMaEmbedSelector use mmap
6% | ███ | 2 / 31 [1.11s<17.25s, 1.80 count/s] embed_selector init ok
96% | ███████████████████████████████ | 30 / 31 [31.91s<32.98s, 0.94 count/s] init 27 axmodel
100% | ████████████████████████████████ | 31 / 31 [36.09s<36.09s, 0.86 count/s] init post axmodel ok,remain_cmm(5068 MB)
[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 : 512
[I][ Init][ 252]: grp: 3, prefill_max_token_num : 1024
[I][ Init][ 252]: grp: 4, prefill_max_token_num : 1536
[I][ Init][ 252]: grp: 5, prefill_max_token_num : 2048
[I][ Init][ 256]: prefill_max_token_num : 2048
________________________
| ID| remain cmm(MB)|
========================
| 0| 5068|
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
[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 >> hello
[I][ SetKVCache][ 628]: prefill_grpid:2 kv_cache_num:512 precompute_len:21 input_num_token:12
[I][ SetKVCache][ 631]: current prefill_max_token_num:1920
[I][ Run][ 869]: input token num : 12, prefill_split_num : 1
[I][ Run][ 901]: input_num_token:12
[I][ Run][1030]: ttft: 670.51 ms
<think>
</think>
Hello! How can I assist you today?
[N][ Run][1182]: hit eos,avg 12.88 token/s
[I][ GetKVCache][ 597]: precompute_len:46, remaining:2002
prompt >>
Model | Quantization | tftt (ms) | token/s |
---|---|---|---|
Qwen3-0.6B | w8a16 | 670.51 | 12.88 |
Qwen3-1.7B | w8a16 | 796.38 | 7.38 |
Qwen2.5-0.5B | w4a16 | - | 27.05 |
Qwen2.5-1.5B | w4a16 | - | 15.06 |