Text Classification
Transformers
Safetensors
English
Chinese
gemma4
image-text-to-text
system-one
jev
typed-decisions
calibrated-classification
decision-model
kiosk
Instructions to use BricksDisplay/jevling-e2b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BricksDisplay/jevling-e2b-v0.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="BricksDisplay/jevling-e2b-v0.1")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("BricksDisplay/jevling-e2b-v0.1") model = AutoModelForMultimodalLM.from_pretrained("BricksDisplay/jevling-e2b-v0.1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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license: gemma
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base_model: google/gemma-4-E2B-it
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language: [en, zh]
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tags: [system-one, jev, typed-decisions, calibrated-classification, kiosk]
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library_name: transformers
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pipeline_tag: text-classification
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---
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license: gemma
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base_model: google/gemma-4-E2B-it
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language: [en, zh]
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tags: [system-one, jev, typed-decisions, calibrated-classification, decision-model, kiosk]
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library_name: transformers
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pipeline_tag: text-classification
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---
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