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release v1.1.1

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# Description
RKLLM software stack can help users to quickly deploy AI models to Rockchip chips. The overall framework is as follows:
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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
- RK3588 Series
- RK3576 Series
# Support Models
- [X] [LLAMA models](https://huggingface.co/meta-llama)
- [X] [TinyLLAMA models](https://huggingface.co/TinyLlama)
- [X] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
- [X] [Phi models](https://huggingface.co/models?search=microsoft/phi)
- [X] [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b/tree/103caa40027ebfd8450289ca2f278eac4ff26405)
- [X] [Gemma models](https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315)
- [X] [InternLM2 models](https://huggingface.co/collections/internlm/internlm2-65b0ce04970888799707893c)
- [X] [MiniCPM models](https://huggingface.co/collections/openbmb/minicpm-65d48bf958302b9fd25b698f)
- [x] [LLAMA models](https://huggingface.co/meta-llama)
- [x] [TinyLLAMA models](https://huggingface.co/TinyLlama)
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
- [x] [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b/tree/103caa40027ebfd8450289ca2f278eac4ff26405)
- [x] [Gemma models](https://huggingface.co/collections/google/gemma-2-release-667d6600fd5220e7b967f315)
- [x] [InternLM2 models](https://huggingface.co/collections/internlm/internlm2-65b0ce04970888799707893c)
- [x] [MiniCPM models](https://huggingface.co/collections/openbmb/minicpm-65d48bf958302b9fd25b698f)
# Model Performance Benchmark
| model | dtype | seqlen | max_context | new_tokens | TTFT(ms) | Tokens/s | memory(G) | platform |
|:-------------- |:---------- |:------:|:-----------:|:----------:|:--------:|:--------:|:---------:|:--------:|
| TinyLLAMA-1.1B | w4a16 | 64 | 320 | 256 | 345.00 | 21.10 | 0.77 | RK3576 |
| | w4a16_g128 | 64 | 320 | 256 | 410.00 | 18.50 | 0.8 | RK3576 |
| | w8a8 | 64 | 320 | 256 | 140.46 | 24.21 | 1.25 | RK3588 |
| | w8a8_g512 | 64 | 320 | 256 | 195.00 | 20.08 | 1.29 | RK3588 |
| Qwen2-1.5B | w4a16 | 64 | 320 | 256 | 512.00 | 14.40 | 1.75 | RK3576 |
| | w4a16_g128 | 64 | 320 | 256 | 550.00 | 12.75 | 1.76 | RK3576 |
| | w8a8 | 64 | 320 | 256 | 206.00 | 16.46 | 2.47 | RK3588 |
| | w8a8_g128 | 64 | 320 | 256 | 725.00 | 7.00 | 2.65 | RK3588 |
| Phi-3-3.8B | w4a16 | 64 | 320 | 256 | 975.00 | 6.60 | 2.16 | RK3576 |
| | w4a16_g128 | 64 | 320 | 256 | 1180.00 | 5.85 | 2.23 | RK3576 |
| | w8a8 | 64 | 320 | 256 | 516.00 | 7.44 | 3.88 | RK3588 |
| | w8a8_g512 | 64 | 320 | 256 | 610.00 | 6.13 | 3.95 | RK3588 |
| ChatGLM3-6B | w4a16 | 64 | 320 | 256 | 1168.00 | 4.62 | 3.86 | RK3576 |
| | w4a16_g128 | 64 | 320 | 256 | 1582.56 | 3.82 | 3.96 | RK3576 |
| | w8a8 | 64 | 320 | 256 | 800.00 | 4.95 | 6.69 | RK3588 |
| | w8a8_g128 | 64 | 320 | 256 | 2190.00 | 2.70 | 7.18 | RK3588 |
| Gemma2-2B | w4a16 | 64 | 320 | 256 | 628.00 | 8.00 | 3.63 | RK3576 |
| | w4a16_g128 | 64 | 320 | 256 | 776.20 | 7.40 | 3.63 | RK3576 |
| | w8a8 | 64 | 320 | 256 | 342.29 | 9.67 | 4.84 | RK3588 |
| | w8a8_g128 | 64 | 320 | 256 | 1055.00 | 5.49 | 5.14 | RK3588 |
| InternLM2-1.8B | w4a16 | 64 | 320 | 256 | 475.00 | 13.30 | 1.59 | RK3576 |
| | w4a16_g128 | 64 | 320 | 256 | 572.00 | 11.95 | 1.62 | RK3576 |
| | w8a8 | 64 | 320 | 256 | 205.97 | 15.66 | 2.38 | RK3588 |
| | w8a8_g512 | 64 | 320 | 256 | 298.00 | 12.66 | 2.45 | RK3588 |
| MiniCPM3-4B | w4a16 | 64 | 320 | 256 | 1397.00 | 4.80 | 2.7 | RK3576 |
| | w4a16_g128 | 64 | 320 | 256 | 1645.00 | 4.39 | 2.8 | RK3576 |
| | w8a8 | 64 | 320 | 256 | 702.18 | 6.15 | 4.65 | RK3588 |
| | w8a8_g128 | 64 | 320 | 256 | 1691.00 | 3.42 | 5.06 | RK3588 |
| llama3-8B | w4a16 | 64 | 320 | 256 | 1607.98 | 3.60 | 5.63 | RK3576 |
| | w4a16_g128 | 64 | 320 | 256 | 2010.00 | 3.00 | 5.76 | RK3576 |
| | w8a8 | 64 | 320 | 256 | 1128.00 | 3.79 | 9.21 | RK3588 |
| | w8a8_g512 | 64 | 320 | 256 | 1281.35 | 3.05 | 9.45 | RK3588 |
- This performance data were collected based on the maximum CPU and NPU frequencies of each platform with version 1.1.0.
- The script for setting the frequencies is located in the scripts directory.
# Download
You can download the latest package, docker image, example, documentation, and platform-tool from [RKLLM_SDK](https://console.zbox.filez.com/l/RJJDmB), fetch code: rkllm
# Note
The modifications in version 1.1.0 are significant, making it incompatible with older version models. Please use the latest toolchain for model conversion and inference.
- The modifications in version 1.1 are significant, making it incompatible with older version models. Please use the latest toolchain for model conversion and inference.
- The supported Python versions are:
- Python 3.8
- Python 3.10
- Latest version: [ <u>v1.1.1](https://github.com/airockchip/rknn-llm/releases/tag/release-v1.1.1)</u>
# RKNN Toolkit2
If you want to deploy additional AI model, we have introduced a SDK called RKNN-Toolkit2. For details, please refer to:
https://github.com/airockchip/rknn-toolkit2
# CHANGELOG
## v1.1.0
- Support group-wise quantization (w4a16 group sizes of 32/64/128, w8a8 group sizes of 128/256/512).
- Support joint inference with LoRA model loading
- Support storage and preloading of prompt cache.