Image Classification
Transformers
Safetensors
qwen3_5
feature-extraction
clef
bitsandbytes
quantized
4-bit precision
Instructions to use Aikimi/clef-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aikimi/clef-nf4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Aikimi/clef-nf4") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("Aikimi/clef-nf4") model = AutoModel.from_pretrained("Aikimi/clef-nf4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download clef_runtime/__init__.py from Aikimi/clef-nf4: direct link, hf CLI and curl.
- Browser
- Download file 72 Bytes
-
https://huggingface.co/Aikimi/clef-nf4/resolve/main/clef_runtime/__init__.py
- Command line
-
hf download hf://Aikimi/clef-nf4/clef_runtime/__init__.py
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curl -L -o __init__.py https://huggingface.co/Aikimi/clef-nf4/resolve/main/clef_runtime/__init__.py
72 Bytes
| """Local Clef decision models and the shared Forge Studio interface.""" | |