Zero-Shot Classification
Transformers
Safetensors
qwen3_5
feature-extraction
decision-model
classification
system-one
multimodal
vision
video
custom_code
Instructions to use vllm-sr/d3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vllm-sr/d3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="vllm-sr/d3", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("vllm-sr/d3", trust_remote_code=True) model = AutoModel.from_pretrained("vllm-sr/d3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from vllm-sr/d3: direct link, hf CLI and curl.
- Browser
- Download file 12.8 MB
-
https://huggingface.co/vllm-sr/d3/resolve/main/tokenizer.json
- Command line
-
hf download hf://vllm-sr/d3/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/vllm-sr/d3/resolve/main/tokenizer.json
12.8 MB
- Xet hash:
- ddca7e708ff0da8902fdcef5209d5eea8bf922715733eb02f93026a37478e426
- Size of remote file:
- 12.8 MB
- SHA256:
- 0997f410c57a1f4e53b09e4be8f4a172d90edd9564368fb0847030937229b9f3
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