Feature Extraction
Transformers
Safetensors
PerceptionEncoder
English
pe_audio_video
audio-text-retrieval
image-text-retrieval
zero-shot-audio-classification
multimodal-embeddings
Eval Results (legacy)
Instructions to use MC7ever/pe-single-L14 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MC7ever/pe-single-L14 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="MC7ever/pe-single-L14")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("MC7ever/pe-single-L14") model = AutoModel.from_pretrained("MC7ever/pe-single-L14", device_map="auto") - PerceptionEncoder
How to use MC7ever/pe-single-L14 with PerceptionEncoder:
# Use any PE model as a vision encoder import core.vision_encoder.pe as pe model = pe.VisionTransformer.from_config("MC7ever/pe-single-L14", pretrained=True) - Notebooks
- Google Colab
- Kaggle
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