Embody_AI with car as Demo

🌐 Website • 🤗 Model

• [2024.02.23] 🎉🎉🎉 MindedWheeler is published！🎉🎉🎉

User：快速向左转
RobotAI: (1.0, -0.3)    
...

• The two float are in range [-1,1]
• The first float is speed, the second is direction (negative means left, positive means right).

• 0x02, 0x02, 0x01, 8, data_buf; (See detail in code)

1. DownLoad 🤗 Model get model.bin.

   cd MindedWheeler
   git submodule update --init --recursive
   python qwen_cpp/convert.py -i {Model_Path} -t {type} -o robot1_8b-ggml.bin
   

   You are free to try any of the below quantization types by specifying -t :

   • q4_0: 4-bit integer quantization with fp16 scales.
   • q4_1: 4-bit integer quantization with fp16 scales and minimum values.
   • q5_0: 5-bit integer quantization with fp16 scales.
   • q5_1: 5-bit integer quantization with fp16 scales and minimum values.
   • q8_0: 8-bit integer quantization with fp16 scales.
   • f16: half precision floating point weights without quantization.
   • f32: single precision floating point weights without quantization.

2. Install package serial.tar.gz

    tar zxvf serial.tar.gz
    cd serial
    cmake .. && make && sudo make install
   

3. Compile the project using CMake:

   cmake -B build
   cmake --build build -j --config Release
   

4. Now you may chat and control your AI car with the quantized RobotAI model by running:

   • qwen.tiktoken is in the model directory

   ./build/bin/main -m robot1_8b-ggml.bin --tiktoken qwen.tiktoken -p 请快速向前
   

   To run the model in interactive mode, add the -i flag. For example:

   ./build/bin/main -m robot1_8b-ggml.bin --tiktoken qwen.tiktoken -i
   

   In interactive mode, your chat history will serve as the context for the next-round conversation.

• Continue to create data and train a robust model
• Add ASR and TTS
• ...

Please use the following citation if you intend to use our dataset for training or evaluation:

@misc{MindedWheeler,
  title={MindedWheeler: Embody_AI with car as Demo},
  author={Xidong Wang*, Yuan Shen*},
  year = {2024},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/FreedomIntelligence/MindedWheeler}},
}

• We thank Qwen.cpp and llama.cpp for their excellent work.