https://github.com/freedomintelligence/mindedwheeler

Embody_AI with car as Demo
https://github.com/freedomintelligence/mindedwheeler

Last synced: 10 months ago
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Embody_AI with car as Demo

• Host: GitHub
• URL: https://github.com/freedomintelligence/mindedwheeler
• Owner: FreedomIntelligence
• License: other
• Created: 2024-02-22T11:28:08.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-02-23T12:55:30.000Z (over 2 years ago)
• Last Synced: 2025-04-30T19:48:10.317Z (about 1 year ago)
• Language: C++
• Size: 2.81 MB
• Stars: 6
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# MindedWheeler

Embody_AI with car as Demo

    



   🌐 Website • 🤗 Model  



## 🌈 Update

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

## 🤖 Model Training Data

```

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).

## 🤖 Communication Protocol

- 0x02, 0x02, 0x01, 8, data_buf;  (See detail in [code](https://github.com/FreedomIntelligence/MindedWheeler/blob/main/qwen.cpp#L151))

    

## ℹ️ Usage

1. DownLoad 🤗 [Model](https://huggingface.co/FreedomIntelligence/MindedWheeler) 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.

## 🥸 To do list

- Continue to create data and train a robust model

- Add ASR and TTS

- ...

## ✨ Citation

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}},

}

```

## 🤖 Acknowledgement 

- We thank [Qwen.cpp](https://github.com/QwenLM/qwen.cpp.git) and [llama.cpp](https://github.com/ggerganov/llama.cpp) for their excellent work.