Zero-Shot Image Classification
Transformers
ONNX
Chinese
English
m2_encoder
feature-extraction
multimodal
image-text-retrieval
bilingual
chinese
english
vision-language
custom-code
custom_code
Eval Results (legacy)
Instructions to use malusama/M2-Encoder-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malusama/M2-Encoder-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="malusama/M2-Encoder-1B", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("malusama/M2-Encoder-1B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download .gitattributes from malusama/M2-Encoder-1B: direct link, hf CLI and curl.
- Browser
- Download file 366 Bytes
-
https://huggingface.co/malusama/M2-Encoder-1B/resolve/main/.gitattributes
- Command line
-
hf download hf://malusama/M2-Encoder-1B/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/malusama/M2-Encoder-1B/resolve/main/.gitattributes
366 Bytes
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