Zero-Shot Classification
Transformers
Safetensors
English
qwen3_5
feature-extraction
multi-label-classification
decision-model
classification
system-one
custom_code
Instructions to use akta-pro/d3-mini-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use akta-pro/d3-mini-multi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="akta-pro/d3-mini-multi", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("akta-pro/d3-mini-multi", trust_remote_code=True) model = AutoModel.from_pretrained("akta-pro/d3-mini-multi", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from akta-pro/d3-mini-multi: direct link, hf CLI and curl.
- Browser
- Download file 12.8 MB
-
https://huggingface.co/akta-pro/d3-mini-multi/resolve/main/tokenizer.json
- Command line
-
hf download hf://akta-pro/d3-mini-multi/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/akta-pro/d3-mini-multi/resolve/main/tokenizer.json
12.8 MB
- Xet hash:
- 655d3f6803c619aa34c31a027ce74a315eaa200aad3fca5b968dcdf6485bfba2
- Size of remote file:
- 12.8 MB
- SHA256:
- 5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42
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