Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
security
code-review
ai-agents
decision-model
d3
system-one
pre-release
custom_code
Instructions to use stonefish-labs/d3-mini-agentcode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stonefish-labs/d3-mini-agentcode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="stonefish-labs/d3-mini-agentcode", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("stonefish-labs/d3-mini-agentcode", trust_remote_code=True) model = AutoModel.from_pretrained("stonefish-labs/d3-mini-agentcode", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 723 Bytes
21a9138 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | # 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). |