README.md

---
license: mit
language:
- zh
- en
base_model:
- Qwen/Qwen2.5-3B-Instruct
- Qwen/Qwen2.5-3B-Instruct-GPTQ-INT8
- Qwen/Qwen2.5-3B-Instruct-GPTQ-INT4
pipeline_tag: text-generation
library_name: transformers
tags:
- Context
- Qwen2.5-3B-Instruct-GPTQ-INT8
- Qwen2.5-3B-Instruct-GPTQ-INT4
---

# Qwen2.5-3B-Instruct

This version of Qwen2.5-3B-Instruct has been converted to run on the Axera NPU using **w8a16** and **w4a16** quantization.

This model has been optimized with the following LoRA: 

Compatible with Pulsar2 version: 4.1

## Feature

- Support for longer contexts, in this sample it's 2k
- Support context dialogue
- System prompt kvcache is supported

## 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](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html) 

[AXera NPU AXEngine LLM Runtime](https://github.com/AXERA-TECH/ax-llm/tree/ax-context) 

[AXera NPU AXCL LLM Runtime](https://github.com/AXERA-TECH/ax-llm/tree/axcl-context) 

### Convert script

The follow show how to convert Qwen2.5-3B-Instruct-GPTQ-Int8

```
pulsar2 llm_build --input_path Qwen/Qwen2.5-3B-Instruct-GPTQ-Int8  \
                  --output_path Qwen/Qwen2.5-3B-Instruct-GPTQ-Int8-ctx-ax650 \
                  --hidden_state_type bf16 --kv_cache_len 2047 --prefill_len 128 \
                  --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 \
                  --chip AX650 -c 1 --parallel 8
```

## Support Platform

- AX650
  - AX650N DEMO Board
  - [M4N-Dock(爱芯派Pro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html)
  - [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html)
  
  

|Chips|w8a16|w4a16| DDR(w8) | Flash(w8) | DDR(w4) | Flash(w4) |
|--|--|--|--|--|--|--|
|AX650|  |  |  |  | 2.8GB | 2.9GB |

## How to use

Download all files from this repository to the device

```
(base) axera@raspberrypi:~/samples/AXERA-TECH/Qwen2.5-3B-Instruct $ tree -L 1
.
├── config.json
├── main_api_ax650
├── main_api_axcl_aarch64
├── main_api_axcl_x86
├── main_ax650
├── main_axcl_aarch64
├── main_axcl_x86
├── post_config.json
├── qwen2.5-3b-ctx-int4-ax650
├── qwen2.5_tokenizer
├── qwen2.5_tokenizer_uid.py
├── README.md
├── run_qwen2.5_3b_ctx_ax650.sh
├── run_qwen2.5_3b_ctx_axcl_aarch64.sh
├── run_qwen2.5_3b_ctx_axcl_x86.sh
├── run_qwen2.5_3b_ctx_int4_ax650.sh
├── run_qwen2.5_3b_ctx_int4_axcl_aarch64.sh
└── run_qwen2.5_3b_ctx_int4_axcl_x86.sh

3 directories, 16 files
```

#### Start the Tokenizer service

```
(py312) axera@raspberrypi:~/samples/AXERA-TECH/Qwen2.5-3B-Instruct $ python qwen2.5_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
```

#### System prompt cache

- The System prompt can be preset through the configuration file from `--system_prompt`
- The System prompt can be cached in the form of kv cache to a specified folder for quick loading at the next run time from `--kvcache_path`
- This folder needs to be created manually before running, for example `mkdir kvcache`

```
./main_axcl_aarch64 \
--template_filename_axmodel "qwen2.5-3b-ctx-int4-ax650/qwen2_p128_l%d_together.axmodel" \
--axmodel_num 36 \
--url_tokenizer_model "http://127.0.0.1:12345" \
--filename_post_axmodel "qwen2.5-3b-ctx-int4-ax650/qwen2_post.axmodel" \
--filename_tokens_embed "qwen2.5-3b-ctx-int4-ax650/model.embed_tokens.weight.bfloat16.bin" \
--tokens_embed_num 151936 \
--tokens_embed_size 2048 \
--use_mmap_load_embed 1 \
--live_print 1 \
--devices 0

#--system_prompt "你的名字叫小智（allen）,你是一个人畜无害的AI助手。深圳市今天（4月1日）阴天，愚人节，气温在14°C至19°C之间，微风。" \
#--kvcache_path "./kvcache" \
```

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

Open another terminal and run `run_qwen2.5_3b_ctx_ax650.sh`

TODO

#### Inference with M.2 Accelerator card

[What is M.2 Accelerator card?](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html), Show this DEMO based on Raspberry PI 5.

```
(base) axera@raspberrypi:~/samples/AXERA-TECH/Qwen2.5-3B-Instruct $ ./run_qwen2.5_3b_ctx_int4_axcl_aarch64.sh
[I][                            Init][ 130]: LLM init start
[I][                            Init][  34]: connect http://127.0.0.1:12345 ok
[I][                            Init][  57]: uid: ec8f8194-c12f-41fa-b1a0-1ae232c9f15a
bos_id: -1, eos_id: 151645
  2% | █                                 |   1 /  39 [0.55s<21.33s, 1.83 count/s] tokenizer init ok[I][                            Init][  45]: LLaMaEmbedSelector use mmap
  5% | ██                                |   2 /  39 [0.55s<10.67s, 3.66 count/s] embed_selector init ok
[I][                             run][  30]: AXCLWorker start with devid 0
 76% | ██████████████████████████████████████████████  █       ██  |  29 /  39 [41.68s<58.05s, 0.67 count/s] init 6 axmodel ok,devid(0) remain_cmm(-1 MB)        |  30 /  39 [41.6100% | ████████████████████████████████ |  39 /  39 [61.29s<64.60s, 0.60 count/s] init post axmodel ok,remain_cmm(3981 MB)4305 MB)
[I][                            Init][ 221]: max_token_len : 2047
[I][                            Init][ 224]: kv_cache_size : 256, kv_cache_num: 2047
[I][                            Init][ 232]: prefill_token_num : 128
[I][                            Init][ 236]: grp: 1, prefill_max_token_num : 1
[I][                            Init][ 236]: grp: 2, prefill_max_token_num : 128
[I][                            Init][ 236]: grp: 3, prefill_max_token_num : 256
[I][                            Init][ 236]: grp: 4, prefill_max_token_num : 384
[I][                            Init][ 236]: grp: 5, prefill_max_token_num : 512
[I][                            Init][ 236]: grp: 6, prefill_max_token_num : 640
[I][                            Init][ 236]: grp: 7, prefill_max_token_num : 768
[I][                            Init][ 236]: grp: 8, prefill_max_token_num : 896
[I][                            Init][ 236]: grp: 9, prefill_max_token_num : 1024
[I][                            Init][ 240]: prefill_max_token_num : 1024
________________________
|    ID| remain cmm(MB)|
========================
|     0|           3981|
¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯¯
[I][                     load_config][ 282]: load config:
{
    "enable_repetition_penalty": false,
    "enable_temperature": true,
    "enable_top_k_sampling": true,
    "enable_top_p_sampling": false,
    "penalty_window": 20,
    "repetition_penalty": 1.2,
    "temperature": 0.9,
    "top_k": 10,
    "top_p": 0.8
}

[I][                            Init][ 263]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
[I][          GenerateKVCachePrefill][ 324]: input token num : 21, prefill_split_num : 1 prefill_grpid : 2
[I][          GenerateKVCachePrefill][ 367]: input_num_token:21
[I][                            main][ 234]: precompute_len: 21
[I][                            main][ 235]: system_prompt: You are Qwen, created by Alibaba Cloud. You are a helpful assistant.
prompt >> who are you
[I][                      SetKVCache][ 614]: prefill_grpid:2 kv_cache_num:128 precompute_len:21 input_num_token:11
[I][                      SetKVCache][ 617]: current prefill_max_token_num:896
[I][                             Run][ 855]: input token num : 11, prefill_split_num : 1
[I][                             Run][ 887]: input_num_token:11
[I][                             Run][1016]: ttft: 596.11 ms
I am Qwen, created by Alibaba Cloud. I am here to assist with a wide range of tasks and answer a variety of questions. How can I assist you today?

[N][                             Run][1168]: hit eos,avg 7.72 token/s

[I][                      GetKVCache][ 583]: precompute_len:67, remaining:957
prompt >> q
```