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98 lines
3.5 KiB
Markdown
98 lines
3.5 KiB
Markdown
# ezrknn-llm
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This repo tries to make RKNN LLM usage easier for people who don't want to read through Rockchip's docs.
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Main repo is https://github.com/Pelochus/ezrknpu where you can find more instructions, documentation... for general use.
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This repo is intended for details in RKLLM and how also how to convert models.
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## Requirements
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Keep in mind this repo is focused for:
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- High-end Rockchip SoCs, mainly the RK3588
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- Linux, not Android
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- Linux kernels from Rockchip (as of writing 5.10 and 6.1 from Rockchip should work, if your board has one of these it will very likely be Rockchip's kernel)
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## Quick Install
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First clone the repo:
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```bash
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git clone https://github.com/Pelochus/ezrknn-llm
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```
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Then run:
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```bash
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cd ezrknn-llm && bash install.sh
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```
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## Test
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Run (assuming your on the folder where your `.rkllm` file is located):
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```bash
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rkllm qwen-chat-1_8B.rkllm # Or any other model you like
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```
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## Converting LLMs for Rockchip's NPUs
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### Docker
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In order to do this, you need a Linux PC x86 (Intel or AMD). Currently, Rockchip does not provide ARM support for converting models, so can't be done on a Orange Pi or similar.
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Run:
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`docker run -it pelochus/ezrkllm-toolkit:latest bash`
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Then, inside the Docker container:
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```bash
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apt install -y python3-tk # This needs some configuring from your part
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cd ezrknn-llm/rkllm-toolkit/examples/huggingface/
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```
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Now change the `test.py` with your preferred model. This container provides Qwen-1.8B and LLaMa2 Uncensored. By default, Qwen-1.8B is selected.
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Before converting the model, remember to run `git lfs pull` to download the model.
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To convert the model, run:
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`python3 test.py`
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# Original README starts below
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<hr>
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<hr>
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<hr>
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# Description
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RKLLM software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows:
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<center class="half">
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<div style="background-color:#ffffff;">
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<img src="res/framework.jpg" title="RKLLM"/>
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</center>
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In order to use RKNPU, users need to first run the RKLLM-Toolkit tool on the computer, convert the trained model into an RKLLM format model, and then inference on the development board using the RKLLM C API.
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- RKLLM-Toolkit is a software development kit for users to perform model conversionand quantization on PC.
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- RKLLM Runtime provides C/C++ programming interfaces for Rockchip NPU platform to help users deploy RKLLM models and accelerate the implementation of LLM applications.
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- RKNPU kernel driver is responsible for interacting with NPU hardware. It has been open source and can be found in the Rockchip kernel code.
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# Support Platform
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- RK3588 Series
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- RK3576 Series
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# Download
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- You can also download all packages, docker image, examples, docs and platform-tools from [RKLLM_SDK](https://console.zbox.filez.com/l/RJJDmB), fetch code: rkllm
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# RKNN Toolkit2
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If you want to deploy additional AI model, we have introduced a new SDK called RKNN-Toolkit2. For details, please refer to:
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https://github.com/airockchip/rknn-toolkit2
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# Notes
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Due to recent updates to the Phi2 model, the current version of the RKLLM SDK does not yet support these changes.
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Please ensure to download a version of the [Phi2](https://hf-mirror.com/microsoft/phi-2/tree/834565c23f9b28b96ccbeabe614dd906b6db551a) model that is supported.
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# CHANGELOG
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## v1.0.0-beta
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- Supports the conversion and deployment of LLM models on RK3588/RK3576 platforms
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- Compatible with Hugging Face model architectures
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- Currently supports the models LLaMA, Qwen, Qwen2, and Phi-2
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- Supports quantization with w8a8 and w4a16 precision
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