mirror of
https://github.com/DrHo1y/ezrknn-llm.git
synced 2026-03-23 17:16:44 +07:00
89 lines
3.2 KiB
Markdown
89 lines
3.2 KiB
Markdown
# 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:
|
|
|
|
```bash
|
|
curl https://raw.githubusercontent.com/Pelochus/ezrknn-llm/main/install.sh | sudo bash
|
|
```
|
|
|
|
## Test
|
|
Run (cd is required):
|
|
|
|
```bash
|
|
# TODO
|
|
```
|
|
|
|
## Converting LLMs for Rockchip's NPUs
|
|
### Docker
|
|
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.
|
|
Run:
|
|
|
|
`docker run -it pelochus/ezrkllm-toolkit:1.0 bash`
|
|
|
|
Then, inside the Docker container:
|
|
|
|
```bash
|
|
apt install -y python3-tk # This needs some configuring from your part
|
|
cd ezrknn-llm/rkllm-toolkit/examples/huggingface/
|
|
```
|
|
|
|
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. To convert the model, run:
|
|
|
|
`python3 test.py`
|
|
|
|
I currently cannot convert the models, so I don't know what the output will be. I believe this is Rockchip's fault. Let me know if you could or what error gives you.
|
|
|
|
# Original README starts below
|
|
|
|
<hr>
|
|
<hr>
|
|
<hr>
|
|
|
|
# Description
|
|
RKLLM software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows:
|
|
<center class="half">
|
|
<div style="background-color:#ffffff;">
|
|
<img src="res/framework.jpg" title="RKLLM"/>
|
|
</center>
|
|
|
|
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.
|
|
|
|
- RKLLM-Toolkit is a software development kit for users to perform model conversionand quantization on PC.
|
|
|
|
- RKLLM Runtime provides C/C++ programming interfaces for Rockchip NPU platform to help users deploy RKLLM models and accelerate the implementation of LLM applications.
|
|
|
|
- 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](https://console.zbox.filez.com/l/RJJDmB), 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](https://hf-mirror.com/microsoft/phi-2/tree/834565c23f9b28b96ccbeabe614dd906b6db551a) 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
|