AXERA-TECH / MiniCPM4-0.5B

MiniCPM4-0.5B-Int8

This version of MiniCPM4-0.5B 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: 4.1(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/openbmb/MiniCPM4-0.5B

Pulsar2 Link, How to Convert LLM from Huggingface to axmodel

AXera NPU LLM Runtime

Support Platform

• AX650
  • M4N-Dock(爱芯派Pro)
  • M.2 Accelerator card
• AX630C
  • 爱芯派2
  • Module-LLM
  • LLM630 Compute Kit

How to use

Download all files from this repository to the device

root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# tree -L 1
.
|-- main_ax650
|-- main_axcl_aarch64
|-- main_axcl_x86
|-- minicpm4-0.5b-int8-ctx-ax650
|-- minicpm4_tokenizer
|-- minicpm4_tokenizer_uid.py
|-- post_config.json
|-- run_minicpm4_0.5b_int8_ctx_ax650.sh
`-- run_minicpm4_0.5b_int8_ctx_axcl_x86.sh
2 directories, 7 files

Start the Tokenizer service

Install requirement

pip install transformers jinja2

root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# python3 minicpm4_tokenizer_uid.py
Server running at http://0.0.0.0:12345

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

Open another terminal and run run_minicpm4_0.5b_int8_ctx_ax650.sh

root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx# ./run_minicpm4_0.5b_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: c779ded0-ff14-4877-869b-1aacc948f2d8
bos_id: 1, eos_id: 73440
100% | ████████████████████████████████ |  27 /  27 [2.53s<2.53s, 10.67 count/s] init post axmodel ok,remain_cmm(4244 MB)
[I][                            Init][ 188]: max_token_len : 1023
[I][                            Init][ 193]: kv_cache_size : 128, kv_cache_num: 1023
[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 : 128
[I][                            Init][ 205]: grp: 3, prefill_max_token_num : 512
[I][                            Init][ 209]: prefill_max_token_num : 512
[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][ 271]: input token num : 25, prefill_split_num : 1 prefill_grpid : 2
[I][          GenerateKVCachePrefill][ 308]: input_num_token:25
[I][                            main][ 230]: precompute_len: 25
[I][                            main][ 231]: system_prompt: You are MiniCPM4, created by ModelBest. You are a helpful assistant.
prompt >> 你是谁?
[I][                      SetKVCache][ 531]: prefill_grpid:2 kv_cache_num:128 precompute_len:25 input_num_token:12
[I][                      SetKVCache][ 534]: current prefill_max_token_num:384
[I][                             Run][ 660]: input token num : 12, prefill_split_num : 1
[I][                             Run][ 686]: input_num_token:12
[I][                             Run][ 829]: ttft: 147.65 ms
你好，我是MiniCPM系列模型，由面壁智能和OpenBMB开源社区开发。详细信息请访问https://github.com/OpenBMB/

[N][                             Run][ 943]: hit eos,avg 35.75 token/s

[I][                      GetKVCache][ 500]: precompute_len:162, remaining:350
prompt >> 9.9与9.11
[I][                      SetKVCache][ 531]: prefill_grpid:3 kv_cache_num:512 precompute_len:162 input_num_token:17
[I][                      SetKVCache][ 534]: current prefill_max_token_num:256
[I][                             Run][ 660]: input token num : 17, prefill_split_num : 1
[I][                             Run][ 686]: input_num_token:17
[I][                             Run][ 829]: ttft: 274.38 ms
9.9比9.11大。

[N][                             Run][ 943]: hit eos,avg 35.44 token/s

[I][                      GetKVCache][ 500]: precompute_len:189, remaining:323
prompt >> q
root@ax650:/mnt/qtang/llm-test/minicpm4-0.5b-ctx#