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
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51ef5ad | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | 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() |