Instructions to use FreedomIntelligence/MindedWheeler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FreedomIntelligence/MindedWheeler with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FreedomIntelligence/MindedWheeler", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("FreedomIntelligence/MindedWheeler", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use FreedomIntelligence/MindedWheeler with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FreedomIntelligence/MindedWheeler" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FreedomIntelligence/MindedWheeler", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FreedomIntelligence/MindedWheeler
- SGLang
How to use FreedomIntelligence/MindedWheeler with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "FreedomIntelligence/MindedWheeler" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FreedomIntelligence/MindedWheeler", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "FreedomIntelligence/MindedWheeler" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FreedomIntelligence/MindedWheeler", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use FreedomIntelligence/MindedWheeler with Docker Model Runner:
docker model run hf.co/FreedomIntelligence/MindedWheeler
| license: apache-2.0 | |
| # MindedWheeler | |
| Embody_AI with car as Demo | |
|  | |
| <p align="center"> | |
| 🌐 <a href="https://github.com/FreedomIntelligence/MindedWheeler" target="_blank">Website</a> • 🤗 <a href="" target="_blank">Model</a> | |
| </p> | |
| ## 🌈 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 <type>: | |
| - 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 | |
| ``` | |
| 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. |