base on # 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 - RK3562 Series - RV1126B Series # Support Models - [x] [LLAMA models](https://huggingface.co/meta-llama) - [x] [TinyLLAMA models](https://huggingface.co/TinyLlama) - [x] [Qwen2/Qwen2.5/Qwen3](https://huggingface.co/Qwen) - [x] [Phi2/Phi3](https://huggingface.co/microsoft) - [x] [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b/tree/103caa40027ebfd8450289ca2f278eac4ff26405) - [x] [Gemma2/Gemma3](https://huggingface.co/google) - [x] [InternLM2 models](https://huggingface.co/collections/internlm/internlm2-65b0ce04970888799707893c) - [x] [MiniCPM3/MiniCPM4](https://huggingface.co/openbmb) - [x] [TeleChat2](https://huggingface.co/Tele-AI) - [x] [Qwen2-VL-2B-Instruct/Qwen2-VL-7B-Instruct/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen) - [x] [MiniCPM-V-2_6](https://huggingface.co/openbmb/MiniCPM-V-2_6) - [x] [DeepSeek-R1-Distill](https://huggingface.co/collections/deepseek-ai/deepseek-r1-678e1e131c0169c0bc89728d) - [x] [Janus-Pro-1B](https://huggingface.co/deepseek-ai/Janus-Pro-1B) - [x] [InternVL2-1B](https://huggingface.co/OpenGVLab/InternVL2-1B) - [x] [SmolVLM](https://huggingface.co/HuggingFaceTB) - [x] [RWKV7](https://huggingface.co/fla-hub) # Model Performance 1. [Benchmark](https://github.com/airockchip/rknn-llm/tree/main/benchmark.md) results of common LLMs. # **Performance Testing Methods** 1. Run the frequency-setting script from the `scripts` directory on the target platform. 2. Execute `export RKLLM_LOG_LEVEL=1` on the device to log model inference performance and memory usage. 3. Use the `eval_perf_watch_cpu.sh` script to measure CPU utilization. 4. Use the `eval_perf_watch_npu.sh` script to measure NPU utilization. # Download 1. You can download the **latest package** from [RKLLM_SDK](https://console.zbox.filez.com/l/RJJDmB), fetch code: rkllm 2. You can download the **converted rkllm model** from [rkllm_model_zoo](https://console.box.lenovo.com/l/l0tXb8), fetch code: rkllm # Examples 1. Multimodel deployment demo: [Qwen2-VL_Demo](https://github.com/airockchip/rknn-llm/tree/main/examples/Qwen2-VL_Demo) 2. API usage demo: [DeepSeek-R1-Distill-Qwen-1.5B_Demo](https://github.com/airockchip/rknn-llm/tree/main/examples/DeepSeek-R1-Distill-Qwen-1.5B_Demo) 3. API server demo: [rkllm_server_demo](https://github.com/airockchip/rknn-llm/tree/main/examples/rkllm_server_demo) 4. Multimodal_Interactive_Dialogue_Demo [Multimodal_Interactive_Dialogue_Demo](https://github.com/airockchip/rknn-llm/tree/main/examples/Multimodal_Interactive_Dialogue_Demo) # Note - The supported Python versions are: - Python 3.8 - Python 3.9 - Python 3.10 - Python 3.11 - Python 3.12 **Note: Before installing package in a Python 3.12 environment, please run the command:** ``` export BUILD_CUDA_EXT=0 ``` - On some platforms, you may encounter an error indicating that **libomp.so** cannot be found. To resolve this, locate the library in the corresponding cross-compilation toolchain and place it in the board's lib directory, at the same level as librkllmrt.so. - RWKV model conversion only supports Python 3.12. Please use `requirements_rwkv7.txt` to set up the pip environment. - Latest version: [ <u>v1.2.1](https://github.com/airockchip/rknn-llm/releases/tag/release-v1.2.1)</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 ## v1.2.1 - Added support for RWKV7, Qwen3, and MiniCPM4 models - Added support for the RV1126B platform - Enabled function calling capability - Enabled cross-attention inference - Optimize the callback function to support pausing inference - Supported multi-batch inference - Optimized KV cache clearing interface - Improved chat template parsing with support for thinking mode selection - Server demo updated to support OpenAI-compatible format - Added return of model inference performance statistics - Supported mrope multimodal position encoding - A new quantization optimization algorithm has been added to improve quantization accuracy for older version, please refer [CHANGELOG](CHANGELOG.md)", Assign "at most 3 tags" to the expected json: {"id":"14106","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"