ezrknn-llm
This repo tries to make RKNN LLM usage easier for people who don't want to read through Rockchip's docs
Requirements
Keep in mind this repo is focused for:
- High-end Rockchip SoCs, mainly the RK3588
- Linux, not Android
- 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)
Quick Install
Run:
curl https://raw.githubusercontent.com/Pelochus/ezrknn-llm/main/install.sh | sudo bash
Test
Run (cd is required):
# TODO
Original README starts below
Description
RKLLM software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows:
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.
Support Platform
- RK3588 Series
- RK3576 Series
Download
- You can also download all packages, docker image, examples, docs and platform-tools from RKLLM_SDK, fetch code: rkllm
RKNN Toolkit2
If you want to deploy additional AI model, we have introduced a new SDK called RKNN-Toolkit2. For details, please refer to:
https://github.com/airockchip/rknn-toolkit2
Notes
Due to recent updates to the Phi2 model, the current version of the RKLLM SDK does not yet support these changes. Please ensure to download a version of the Phi2 model that is supported.
CHANGELOG
v1.0.0-beta
- Supports the conversion and deployment of LLM models on RK3588/RK3576 platforms
- Compatible with Hugging Face model architectures
- Currently supports the models LLaMA, Qwen, Qwen2, and Phi-2
- Supports quantization with w8a8 and w4a16 precision