You can deploy this model and start earning money today!

AXERA-TECH/
DeepSeek-R1-Distill-Qwen-7B

Name: DeepSeek-R1-Distill-Qwen-7B
Availability: InStock

transformers

bsd-3-clause

endpoints_compatible

Views

No views yet

DeepSeek-R1-Distill-Qwen-7B-AX650

This version of DeepSeek-R1-Distill-Qwen-7B 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: 4.2
Due to the current quantization scheme of w8a16, the CMM consumes about 7.6GiB of memory, so a 16GiB development board is required to run.

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 : https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B and https://huggingface.co/jakiAJK/DeepSeek-R1-Distill-Qwen-7B_GPTQ-int4

Pulsar2 Link, How to Convert LLM from Huggingface to axmodel

AXera NPU AXEngine LLM Runtime

AXera NPU AXCL LLM Runtime

Convert script

The follow show how to convert DeepSeek-R1-Distill-Qwen-7B

pulsar2 llm_build --input_path deepseek-ai/DeepSeek-R1-Distill-Qwen-7B  \
                  --output_path deepseek-ai/DeepSeek-R1-Distill-Qwen-7B-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 \
                  --last_kv_cache_len 1152 \
                  --last_kv_cache_len 1280 \
                  --last_kv_cache_len 1408 \
                  --last_cache_len 1536 \
                  --chip AX650 -c 1 --parallel 8

Support Platform

AX650
- AX650N DEMO Board
- M4N-Dock(爱芯派Pro)
- M.2 Accelerator card
AX630C
- TBD

Chips	w8a16	w4a16
AX650	2.6 tokens/sec	4.8 tokens/sec

How to use

Download all files from this repository to the device

root@ax650:~/wangli/huggingface/DeepSeek-R1-Distill-Qwen-7B# tree -L 1
.
|-- README.md
|-- config.json
|-- deepseek-r1-7b-ax650
|-- deepseek-r1-7b-int4-ax650
|-- deepseek-r1_tokenizer
|-- deepseek-r1_tokenizer.py
|-- main_ax650
|-- main_axcl_aarch64
|-- main_axcl_x86
|-- post_config.json
|-- run_deepseek-r1_7b_ax650.sh
|-- run_deepseek-r1_7b_axcl_aarch64.sh
|-- run_deepseek-r1_7b_axcl_x86.sh
|-- run_deepseek-r1_7b_int4_ax650.sh
|-- run_deepseek-r1_7b_int4_axcl_aarch64.sh
`-- run_deepseek-r1_7b_int4_axcl_x86.sh

3 directories, 13 files

Start the Tokenizer service

root@ax650:~/wangli/huggingface/DeepSeek-R1-Distill-Qwen-7B# python3 deepseek-r1_tokenizer.py
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

root@ax650:~/wangli/huggingface/DeepSeek-R1-Distill-Qwen-7B# cat ./run_deepseek-r1_7b_int4_ax650.sh
./main_ax650 \
--template_filename_axmodel "deepseek-r1-7b-int4-ax650/qwen2_p128_l%d_together.axmodel" \
--axmodel_num 28 \
--url_tokenizer_model "http://127.0.0.1:12345" \
--filename_post_axmodel "deepseek-r1-7b-int4-ax650/qwen2_post.axmodel" \
--filename_tokens_embed "deepseek-r1-7b-int4-ax650/model.embed_tokens.weight.bfloat16.bin" \
--tokens_embed_num 152064 \
--tokens_embed_size 3584 \
--use_mmap_load_embed 1 \
--live_print 1

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

Open another terminal and run run_deepseek-r1_7b_int4_ax650.sh

root@ax650:~/huggingface/DeepSeek-R1-Distill-Qwen-7B# ./run_deepseek-r1_7b_int4_ax650.sh
[I][                            Init][ 110]: LLM init start
[I][                            Init][  34]: connect http://127.0.0.1:12345 ok
[I][                            Init][  57]: uid: e034d25e-4fcb-4c3b-b19a-df31c278d9a8
bos_id: 151646, eos_id: 151643
  3% | ██                                |   1 /  31 [2.16s<67.02s, 0.46 count/s] tokenizer init ok[I][                            Init][  26]: LLaMaEmbedSelector use mmap
100% | ████████████████████████████████ |  31 /  31 [21.75s<21.75s, 1.43 count/s] init post axmodel ok,remain_cmm(4189 MB)[I][                            Init][ 188]: max_token_len : 2047
[I][                            Init][ 193]: kv_cache_size : 512, kv_cache_num: 2047
[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 : 256
[I][                            Init][ 205]: grp: 4, prefill_max_token_num : 384
[I][                            Init][ 205]: grp: 5, prefill_max_token_num : 512
[I][                            Init][ 205]: grp: 6, prefill_max_token_num : 640
[I][                            Init][ 205]: grp: 7, prefill_max_token_num : 768
[I][                            Init][ 205]: grp: 8, prefill_max_token_num : 896
[I][                            Init][ 205]: grp: 9, prefill_max_token_num : 1024
[I][                            Init][ 209]: prefill_max_token_num : 1024
[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][ 218]: LLM init ok
Type "q" to exit, Ctrl+c to stop current running
[I][          GenerateKVCachePrefill][ 275]: input token num : 13, prefill_split_num : 1 prefill_grpid : 2
[I][          GenerateKVCachePrefill][ 315]: input_num_token:13
[I][                            main][ 228]: precompute_len: 13
[I][                            main][ 229]: system_prompt:
prompt >> 你是谁
[I][                      SetKVCache][ 529]: prefill_grpid:2 kv_cache_num:128 precompute_len:13 input_num_token:6
[I][                      SetKVCache][ 532]: current prefill_max_token_num:896
[I][                             Run][ 658]: input token num : 6, prefill_split_num : 1
[I][                             Run][ 684]: input_num_token:6
[I][                             Run][ 807]: ttft: 764.85 ms
Alright, the user greeted me by saying, "You are DeepSeek. You are a helpful assistant." I need to respond in a friendly and professional manner. I should acknowledge that I'm DeepSeek, an AI assistant, and offer assistance. I'll keep it concise and welcoming.
</think>

您好！我是DeepSeek，一个由深度求索公司开发的智能助手。我随时准备为您提供帮助和解答。请问有什么可以为您服务的？

[N][                             Run][ 921]: hit eos,avg 4.87 token/s

[I][                      GetKVCache][ 498]: precompute_len:110, remaining:914
prompt >> q

AXERA-TECH/DeepSeek-R1-Distill-Qwen-7B