Instructions to use FreedomIntelligence/MindedWheeler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

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)

ℹ️ Usage

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.

Install package serial.tar.gz

 cd serial
 cmake .. & make & sudo make install

Compile the project using CMake:

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

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 and llama.cpp for their excellent work.

Downloads last month: 80

Safetensors

Model size

2B params

Tensor type

BF16