Instructions to use ryefoxlime/TADBot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ryefoxlime/TADBot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ryefoxlime/TADBot")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ryefoxlime/TADBot", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ryefoxlime/TADBot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ryefoxlime/TADBot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ryefoxlime/TADBot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ryefoxlime/TADBot
- SGLang
How to use ryefoxlime/TADBot 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 "ryefoxlime/TADBot" \ --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": "ryefoxlime/TADBot", "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 "ryefoxlime/TADBot" \ --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": "ryefoxlime/TADBot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ryefoxlime/TADBot with Docker Model Runner:
docker model run hf.co/ryefoxlime/TADBot
| from detectfaces import fer | |
| from models.PosterV2_7cls import pyramid_trans_expr2 | |
| import os | |
| import torch | |
| from main import RecorderMeter1, RecorderMeter # noqa: F401 | |
| script_dir = os.path.dirname(os.path.abspath(__file__)) | |
| # Construct the full path to the model file | |
| model_path = os.path.join(script_dir,"models","checkpoints","raf-db-model_best.pth") | |
| # Determine the available device for model execution | |
| if torch.backends.mps.is_available(): | |
| device = "mps" | |
| elif torch.cuda.is_available(): | |
| device = "cuda" | |
| else: | |
| device = "cpu" | |
| # Initialize the model with specified image size and number of classes | |
| model = pyramid_trans_expr2(img_size=224, num_classes=7) | |
| # Wrap the model with DataParallel for potential multi-GPU usage | |
| model = torch.nn.DataParallel(model) | |
| # Move the model to the chosen device | |
| model = model.to(device) | |
| def main(): | |
| fer(model_path=model_path, device=device, model=model) | |
| if __name__ == "__main__": | |
| main() |