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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: |
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- HuggingFaceTB/SmolLM2-360M-Instruct |
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tags: |
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- HuggingFaceTB |
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- SmolLM2 |
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- SmolLM2-360M-Instruct |
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- Int8 |
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- M5Stack |
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- RaspberryPi 5 |
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language: |
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- en |
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--- |
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# SmolLM2-360M-Instruct |
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This version of SmolLM2-360M-Instruct has been converted to run on the Axera NPU using **w8a16** quantization. |
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This model has been optimized with the following LoRA: |
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Compatible with Pulsar2 version: 3.4(Not released yet) |
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## Convert tools links: |
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For those who are interested in model conversion, you can try to export axmodel through the original repo |
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https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct |
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[Pulsar2 Link, How to Convert LLM from Huggingface to axmodel](https://pulsar2-docs.readthedocs.io/en/latest/appendix/build_llm.html) |
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[AXera NPU HOST LLM Runtime](https://github.com/AXERA-TECH/ax-llm/tree/internvl2) |
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[AXera NPU AXCL LLM Runtime](https://github.com/AXERA-TECH/ax-llm/tree/axcl-llm-internvl) |
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## Support Platform |
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- AX650 |
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- AX650N DEMO Board |
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- [M4N-Dock(η±θ―ζ΄ΎPro)](https://wiki.sipeed.com/hardware/zh/maixIV/m4ndock/m4ndock.html) |
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- [M.2 Accelerator card](https://axcl-docs.readthedocs.io/zh-cn/latest/doc_guide_hardware.html) |
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- AX630C |
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- [η±θ―ζ΄Ύ2](https://axera-pi-2-docs-cn.readthedocs.io/zh-cn/latest/index.html) |
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- [Module-LLM](https://docs.m5stack.com/zh_CN/module/Module-LLM) |
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- [LLM630 Compute Kit](https://docs.m5stack.com/zh_CN/core/LLM630%20Compute%20Kit) |
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|Chips|w8a16|w4a16| |
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|--|--|--| |
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|AX650| 39 tokens/sec|todo| |
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|AX630C| 14 tokens/sec|todo| |
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## How to use |
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Download all files from this repository to the device |
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``` |
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root@ax650:/mnt/qtang/llm-test/smollm2-360m# tree -L 1 |
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. |
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|-- main_axcl_aarch64 |
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|-- main_axcl_x86 |
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|-- main_prefill |
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|-- post_config.json |
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|-- run_smollm2_360m_ax630c.sh |
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|-- run_smollm2_360m_ax650.sh |
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|-- run_smollm2_360m_axcl_aarch64.sh |
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|-- run_smollm2_360m_axcl_x86.sh |
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|-- smollm2-360m-ax630c |
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|-- smollm2-360m-ax650 |
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|-- smollm2_tokenizer |
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`-- smollm2_tokenizer.py |
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``` |
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### Install transformer |
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``` |
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pip install transformers==4.41.1 |
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``` |
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### Start the Tokenizer service |
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``` |
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root@ax650:/mnt/qtang/llm-test/smollm2-360m$ python smollm2_tokenizer.py --port 12345 |
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1 <|im_start|> 2 <|im_end|> |
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<|im_start|>system |
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You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|> |
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<|im_start|>user |
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hello world<|im_end|> |
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<|im_start|>assistant |
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[1, 9690, 198, 2683, 359, 253, 5356, 5646, 11173, 3365, 3511, 308, 34519, 28, 7018, 411, 407, 19712, 8182, 2, 198, 1, 4093, 198, 28120, 905, 2, 198, 1, 520, 9531, 198] |
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http://localhost:12345 |
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``` |
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### Inference with AX650 Host, such as M4N-Dock(η±θ―ζ΄ΎPro) or AX650N DEMO Board |
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Open another terminal and run `run_smollm2_360m_ax650.sh` |
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``` |
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root@ax650:/mnt/qtang/llm-test/smollm2-360m# ./run_smollm2_360m_ax650.sh |
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[I][ Init][ 125]: LLM init start |
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bos_id: 1, eos_id: 2 |
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2% | β | 1 / 35 [0.00s<0.14s, 250.00 count/s] tokenizer init ok |
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[I][ Init][ 26]: LLaMaEmbedSelector use mmap |
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100% | ββββββββββββββββββββββββββββββββ | 35 / 35 [0.81s<0.81s, 43.37 count/s] init post axmodel ok,remain_cmm(3339 MB) |
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[I][ Init][ 241]: max_token_len : 1023 |
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[I][ Init][ 246]: kv_cache_size : 320, kv_cache_num: 1023 |
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[I][ Init][ 254]: prefill_token_num : 128 |
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[I][ load_config][ 281]: load config: |
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{ |
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"enable_repetition_penalty": false, |
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"enable_temperature": true, |
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"enable_top_k_sampling": true, |
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"enable_top_p_sampling": false, |
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"penalty_window": 20, |
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"repetition_penalty": 1.2, |
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"temperature": 0.9, |
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"top_k": 10, |
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"top_p": 0.8 |
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} |
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[I][ Init][ 268]: LLM init ok |
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Type "q" to exit, Ctrl+c to stop current running |
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>> who are you? |
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[I][ Run][ 466]: ttft: 156.63 ms |
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I'm a chatbot developed by the Artificial Intelligence Research and Development Lab (AI R&D Lab) at Hugging Face Labs, |
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specifically designed to facilitate and augment human-AI conversations. My role is to provide assistance in understanding |
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and responding to natural language queries, using advanced language models and AI algorithms to understand context and intent. |
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[N][ Run][ 605]: hit eos,avg 38.70 token/s |
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>> q |
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``` |
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### Inference with M.2 Accelerator card |
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[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. |
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``` |
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(base) axera@raspberrypi:~/samples/smollm2-360m $ ./run_smollm2_360m_axcl_aarch64.sh |
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build time: Feb 13 2025 15:44:57 |
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[I][ Init][ 111]: LLM init start |
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bos_id: 1, eos_id: 2 |
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100% | ββββββββββββββββββββββββββββββββ | 35 / 35 [18.07s<18.07s, 1.94 count/s] init post axmodel okremain_cmm(6621 MB) |
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[I][ Init][ 226]: max_token_len : 1023 |
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[I][ Init][ 231]: kv_cache_size : 320, kv_cache_num: 1023 |
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[I][ load_config][ 282]: load config: |
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{ |
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"enable_repetition_penalty": false, |
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"enable_temperature": true, |
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"enable_top_k_sampling": true, |
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"enable_top_p_sampling": false, |
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"penalty_window": 20, |
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"repetition_penalty": 1.2, |
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"temperature": 0.9, |
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"top_k": 10, |
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"top_p": 0.8 |
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} |
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[I][ Init][ 288]: LLM init ok |
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Type "q" to exit, Ctrl+c to stop current running |
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>> who are you? |
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I'm a virtual AI assistant, designed to support users with their questions and tasks. |
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I was trained on a vast dataset of text, including text from various sources and |
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conversations. This extensive training allows me to understand and respond to a wide range of queries. |
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I'm here to be helpful and provide answers to your questions. |
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[N][ Run][ 610]: hit eos,avg 20.81 token/s |
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>> ^Cq |
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(base) axera@raspberrypi:~ $ axcl-smi |
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+------------------------------------------------------------------------------------------------+ |
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| AXCL-SMI V2.26.0_20250205130139 Driver V2.26.0_20250205130139 | |
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+-----------------------------------------+--------------+---------------------------------------+ |
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| Card Name Firmware | Bus-Id | Memory-Usage | |
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| Fan Temp Pwr:Usage/Cap | CPU NPU | CMM-Usage | |
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|=========================================+==============+=======================================| |
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| 0 AX650N V2.26.0 | 0000:01:00.0 | 171 MiB / 945 MiB | |
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| -- 39C -- / -- | 2% 0% | 468 MiB / 7040 MiB | |
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+-----------------------------------------+--------------+---------------------------------------+ |
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+------------------------------------------------------------------------------------------------+ |
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| Processes: | |
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| Card PID Process Name NPU Memory Usage | |
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|================================================================================================| |
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| 0 18636 /home/axera/qtang/llm-test/smollm2-360m/main_axcl_aarch64 418580 KiB | |
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+------------------------------------------------------------------------------------------------+ |
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(base) axera@raspberrypi:~ $ |
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``` |
