Instructions to use tiny-random/d3-turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tiny-random/d3-turbo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tiny-random/d3-turbo", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("tiny-random/d3-turbo", trust_remote_code=True) model = AutoModel.from_pretrained("tiny-random/d3-turbo", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download requirements.txt from tiny-random/d3-turbo: direct link, hf CLI and curl.
- Browser
- Download file 723 Bytes
-
https://huggingface.co/tiny-random/d3-turbo/resolve/main/requirements.txt
- Command line
-
hf download hf://tiny-random/d3-turbo/requirements.txt
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curl -L -o requirements.txt https://huggingface.co/tiny-random/d3-turbo/resolve/main/requirements.txt
723 Bytes
| # d3 runtime. Validated with transformers 5.17.0 on ROCm (torch 2.13). | |
| torch>=2.8 | |
| transformers==5.17.0 | |
| accelerate>=1.10 | |
| safetensors>=0.6 | |
| huggingface_hub>=1.0 | |
| # GPU kernels for the Gated DeltaNet layers; without them transformers runs its slower PyTorch reference. | |
| flash-linear-attention==0.5.2 | |
| einops | |
| # Image inputs: PIL decodes the images; the processor's torchvision backend resizes them as in evaluation | |
| # (install the torchvision build that matches torch; without it transformers falls back to a PIL resize). | |
| pillow>=10 | |
| torchvision | |
| # Video files: OpenCV decodes them with FFmpeg (frame arrays need no decoder). | |
| opencv-python-headless | |
| # d3_server.py needs nothing beyond the above (Python standard library HTTP server). | |