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Qwen3-4B-GPTQ-Int4

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Qwen3-4B-GPTQ-Int4

This version of Qwen3-4B-GPTQ-Int4 has been converted to run on the Axera NPU using w4a16 quantization.

This model has been optimized with the following LoRA:

Compatible with Pulsar2 version: 5.2

Convert tools links:

For those who are interested in model conversion, you can try to export axmodel through the original repo : https://huggingface.co/Qwen/Qwen3-4B

Pulsar2 Link, How to Convert LLM from Huggingface to axmodel

AXera NPU LLM Runtime

Convert the original Huggingface Qwen3-4B-GPTQ-Int4 to axmodel, and then apply the w4a16 quantization to get the final axmodel for axllm runtime.

export FLOAT_MATMUL_USE_CONV_EU=1 # only support AX650, for better performance, please set this env var before running the conversion command.

# context window size 2048, prefill length 1024
pulsar2 llm_build --input_path Qwen3-4B-GPTQ-Int4 --output_path <your path> \
--hidden_state_type bf16 --kv_cache_len 2048 --prefill_len 128 --chip AX650 -c 1 --parallel 32 \
--last_kv_cache_len 128 --last_kv_cache_len 256 --last_kv_cache_len 384 --last_kv_cache_len 512 \
--last_kv_cache_len 640 --last_kv_cache_len 768 --last_kv_cache_len 896 --last_kv_cache_len 1024 -w s4

Support Platform

Chips w4a16 CMM Flash
AX650 6.5 tokens/sec 3.4 GiB 3.5 GiB

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/Qwen3-4B-GPTQ-Int4
cd AXERA-TECH/Qwen3-4B-GPTQ-Int4
hf download AXERA-TECH/Qwen3-4B-GPTQ-Int4 --local-dir .

# structure of the downloaded files
.
└── AXERA-TECH
    └── Qwen3-4B-GPTQ-Int4
        ├── README.md
        ├── config.json
        ├── model.embed_tokens.weight.bfloat16.bin
        ├── post_config.json
        ├── qwen3_p128_l0_together.axmodel
...
        ├── qwen3_p128_l9_together.axmodel
        ├── qwen3_post.axmodel
        └── qwen3_tokenizer.txt

2 directories, 42 files

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

运行（CLI） 

(base) root@ax650:~# axllm run AXERA-TECH/Qwen3-4B-GPTQ-Int4/
16:18:26.797 INF Init:890 | LLM init start
tokenizer_type = 1
 97% | ###############################  |  38 /  39 [8.06s<8.27s, 4.72 count/s] init post axmodel ok,remain_cmm(6572 MB)
16:18:34.855 INF Init:1045 | max_token_len : 2048
16:18:34.855 INF Init:1048 | kv_cache_size : 1024, kv_cache_num: 2048
16:18:34.855 INF init_groups_from_model:606 | prefill_token_num : 128
16:18:34.855 INF init_groups_from_model:820 | decode grp: 0, gid: 0, max_token_len : 2048
16:18:34.855 INF init_groups_from_model:824 | prefill grp: 0, gid: 1, history_cap: 0, total_cap: 128, symbolic_cap: 1
16:18:34.855 INF init_groups_from_model:824 | prefill grp: 1, gid: 2, history_cap: 128, total_cap: 256, symbolic_cap: 128
16:18:34.855 INF init_groups_from_model:824 | prefill grp: 2, gid: 3, history_cap: 256, total_cap: 384, symbolic_cap: 256
16:18:34.855 INF init_groups_from_model:824 | prefill grp: 3, gid: 4, history_cap: 384, total_cap: 512, symbolic_cap: 384
16:18:34.855 INF init_groups_from_model:824 | prefill grp: 4, gid: 5, history_cap: 512, total_cap: 640, symbolic_cap: 512
16:18:34.855 INF init_groups_from_model:824 | prefill grp: 5, gid: 6, history_cap: 640, total_cap: 768, symbolic_cap: 640
16:18:34.855 INF init_groups_from_model:824 | prefill grp: 6, gid: 7, history_cap: 768, total_cap: 896, symbolic_cap: 768
16:18:34.855 INF init_groups_from_model:824 | prefill grp: 7, gid: 8, history_cap: 896, total_cap: 1024, symbolic_cap: 896
16:18:34.855 INF init_groups_from_model:824 | prefill grp: 8, gid: 9, history_cap: 1024, total_cap: 1152, symbolic_cap: 1024
16:18:34.855 INF init_groups_from_model:831 | prefill_max_token_num : 1152
16:18:34.855 INF Init:27 | LLaMaEmbedSelector use mmap
100% | ################################ |  39 /  39 [8.06s<8.06s, 4.84 count/s] embed_selector init ok
16:18:34.859 INF load_config:282 | load config:
16:18:34.859 INF load_config:282 | {
16:18:34.859 INF load_config:282 |     "enable_repetition_penalty": false,
16:18:34.859 INF load_config:282 |     "enable_temperature": false,
16:18:34.859 INF load_config:282 |     "enable_top_k_sampling": false,
16:18:34.859 INF load_config:282 |     "enable_top_p_sampling": false,
16:18:34.859 INF load_config:282 |     "penalty_window": 20,
16:18:34.859 INF load_config:282 |     "repetition_penalty": 1.2,
16:18:34.859 INF load_config:282 |     "temperature": 0.9,
16:18:34.859 INF load_config:282 |     "top_k": 10,
16:18:34.859 INF load_config:282 |     "top_p": 0.8
16:18:34.859 INF load_config:282 | }
16:18:34.859 INF Init:1139 | LLM init ok
Commands:
  /q, /exit  退出
  /reset     重置 kvcache
  /dd        删除一轮对话
  /pp        打印历史对话
Ctrl+C: 停止当前生成
----------------------------------------
prompt >> who are you
16:18:40.122 INF SetKVCache:1437 | decode_grpid:0 prefill_grpid:1 history_cap:0 total_cap:128 symbolic_cap:1 precompute_len:0 input_num_token:22 prefer_symbolic_group:0
16:18:40.122 INF SetKVCache:1458 | current prefill_max_token_num:1152
16:18:40.245 INF SetKVCache:1462 | first run
16:18:40.247 INF Run:1553 | input token num : 22, prefill_split_num : 1
16:18:40.247 INF Run:1640 | prefill chunk p=0 history_len=0 grpid=1 kv_cache_num=0 input_tokens=22
16:18:40.247 INF Run:1665 | prefill indices shape: p=0 idx_elems=128 idx_rows=1 pos_rows=0
16:18:40.567 INF Run:1837 | ttft: 320.08 ms
<think>
Okay, the user asked, "who are you?" I need to explain who I am. Let me start by stating my name, Qwen. I should mention that I'm a large language model developed by Alibaba Cloud. My main purpose is to assist with various tasks and answer questions. I should highlight my capabilities, like answering questions, writing articles, and creating content. Also, I need to mention that I can help with multiple languages and that I'm designed to be helpful and friendly. I should keep the tone positive and encouraging. Let me make sure I cover all the key points without being too technical. Maybe add something about being available 24/7. Wait, the user might be interested in knowing my training data or how I work, but maybe that's too detailed. Stick to the basics. Alright, that should cover it.
</think>

Hello! I'm Qwen, a large language model developed by Alibaba Cloud. I'm designed to help with a wide range of tasks, such as answering questions, writing articles, creating content, and more. I can communicate in multiple languages and am here to assist you in any way I can. If you have any questions or need help, feel free to ask! 😊

16:19:19.093 NTC Run:2102 | hit eos,decode avg 6.49 token/s
16:19:19.094 INF GetKVCache:1408 | precompute_len:273, remaining:879
prompt >> /q

启动服务（OpenAI 兼容） 

(base) root@ax650:~# axllm serve AXERA-TECH/Qwen3-4B-GPTQ-Int4/
16:20:02.308 INF Init:890 | LLM init start
tokenizer_type = 1
 97% | ###############################  |  38 /  39 [4.45s<4.57s, 8.54 count/s] init post axmodel ok,remain_cmm(6572 MB)
16:20:06.758 INF Init:1045 | max_token_len : 2048
16:20:06.758 INF Init:1048 | kv_cache_size : 1024, kv_cache_num: 2048
16:20:06.758 INF init_groups_from_model:606 | prefill_token_num : 128
16:20:06.758 INF init_groups_from_model:820 | decode grp: 0, gid: 0, max_token_len : 2048
16:20:06.758 INF init_groups_from_model:824 | prefill grp: 0, gid: 1, history_cap: 0, total_cap: 128, symbolic_cap: 1
16:20:06.758 INF init_groups_from_model:824 | prefill grp: 1, gid: 2, history_cap: 128, total_cap: 256, symbolic_cap: 128
16:20:06.758 INF init_groups_from_model:824 | prefill grp: 2, gid: 3, history_cap: 256, total_cap: 384, symbolic_cap: 256
16:20:06.758 INF init_groups_from_model:824 | prefill grp: 3, gid: 4, history_cap: 384, total_cap: 512, symbolic_cap: 384
16:20:06.758 INF init_groups_from_model:824 | prefill grp: 4, gid: 5, history_cap: 512, total_cap: 640, symbolic_cap: 512
16:20:06.758 INF init_groups_from_model:824 | prefill grp: 5, gid: 6, history_cap: 640, total_cap: 768, symbolic_cap: 640
16:20:06.758 INF init_groups_from_model:824 | prefill grp: 6, gid: 7, history_cap: 768, total_cap: 896, symbolic_cap: 768
16:20:06.758 INF init_groups_from_model:824 | prefill grp: 7, gid: 8, history_cap: 896, total_cap: 1024, symbolic_cap: 896
16:20:06.758 INF init_groups_from_model:824 | prefill grp: 8, gid: 9, history_cap: 1024, total_cap: 1152, symbolic_cap: 1024
16:20:06.758 INF init_groups_from_model:831 | prefill_max_token_num : 1152
16:20:06.758 INF Init:27 | LLaMaEmbedSelector use mmap
100% | ################################ |  39 /  39 [4.45s<4.45s, 8.76 count/s] embed_selector init ok
16:20:06.758 INF load_config:282 | load config:
16:20:06.758 INF load_config:282 | {
16:20:06.758 INF load_config:282 |     "enable_repetition_penalty": false,
16:20:06.758 INF load_config:282 |     "enable_temperature": false,
16:20:06.758 INF load_config:282 |     "enable_top_k_sampling": false,
16:20:06.758 INF load_config:282 |     "enable_top_p_sampling": false,
16:20:06.758 INF load_config:282 |     "penalty_window": 20,
16:20:06.758 INF load_config:282 |     "repetition_penalty": 1.2,
16:20:06.758 INF load_config:282 |     "temperature": 0.9,
16:20:06.758 INF load_config:282 |     "top_k": 10,
16:20:06.758 INF load_config:282 |     "top_p": 0.8
16:20:06.758 INF load_config:282 | }
16:20:06.758 INF Init:1139 | LLM init ok
Starting server on port 8000 with model 'AXERA-TECH/Qwen3-4B-GPTQ-Int4'...
API URLs:
  GET  http://127.0.0.1:8000/health
  GET  http://127.0.0.1:8000/v1/models
  POST http://127.0.0.1:8000/v1/chat/completions
  GET  http://10.126.29.54:8000/health
  GET  http://10.126.29.54:8000/v1/models
  POST http://10.126.29.54:8000/v1/chat/completions
  GET  http://172.17.0.1:8000/health
  GET  http://172.17.0.1:8000/v1/models
  POST http://172.17.0.1:8000/v1/chat/completions
Aliases:
  GET  http://127.0.0.1:8000/models
  POST http://127.0.0.1:8000/chat/completions
  GET  http://10.126.29.54:8000/models
  POST http://10.126.29.54:8000/chat/completions
  GET  http://172.17.0.1:8000/models
  POST http://172.17.0.1:8000/chat/completions
OpenAI API Server starting on http://0.0.0.0:8000
Max concurrency: 1
Models: AXERA-TECH/Qwen3-4B-GPTQ-Int4

OpenAI 调用示例 

from openai import OpenAI

API_URL = "http://127.0.0.1:8000/v1"
MODEL = "AXERA-TECH/Qwen3-4B-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 流式调用示例 

from openai import OpenAI

API_URL = "http://127.0.0.1:8000/v1"
MODEL = "AXERA-TECH/Qwen3-4B-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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