Zero-Shot Image Classification
Transformers
ONNX
Chinese
English
m2_encoder
image-feature-extraction
feature-extraction
multimodal
image-text-retrieval
bilingual
chinese
english
vision-language
custom-code
custom_code
Eval Results (legacy)
Instructions to use malusama/M2-Encoder-0.4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use malusama/M2-Encoder-0.4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="malusama/M2-Encoder-0.4B", trust_remote_code=True) pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("malusama/M2-Encoder-0.4B", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 551cd6b452c01e3912a19a4a3fa64ce6093e3b66014957863aa75af9c329361b
- Size of remote file:
- 1.49 GB
- SHA256:
- f2a4aa8d4429183a9e291c960166331f6cfb04b9e7e360ccb7630a33a7ea7547
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