Zero-Shot Classification
Transformers
Safetensors
glance_gemma
feature-extraction
glance
statement-tuning
multimodal
image-text-matching
custom_code
Instructions to use stefra/embeddinggemma2-glance-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stefra/embeddinggemma2-glance-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-classification", model="stefra/embeddinggemma2-glance-v1", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("stefra/embeddinggemma2-glance-v1", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from stefra/embeddinggemma2-glance-v1: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/stefra/embeddinggemma2-glance-v1/resolve/main/tokenizer.json
- Command line
-
hf download hf://stefra/embeddinggemma2-glance-v1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/stefra/embeddinggemma2-glance-v1/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- 10b9c2824fd5f1f80bc8d6ed210ffae519464dcf45e6fd6a87f895ddf40356b4
- Size of remote file:
- 32.2 MB
- SHA256:
- b2239aee20262352ab56495a790b722ed3d29b3cba2887d2c0404a937c5d6927
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