Text Classification
Transformers
Safetensors
English
Chinese
gemma4
image-text-to-text
system-one
jev
typed-decisions
calibrated-classification
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
Download system_one.json from BricksDisplay/jevling-e2b-v0.1: direct link, hf CLI and curl.
- Browser
- Download file 164 Bytes
-
https://huggingface.co/BricksDisplay/jevling-e2b-v0.1/resolve/main/system_one.json
- Command line
-
hf download hf://BricksDisplay/jevling-e2b-v0.1/system_one.json
-
curl -L -o system_one.json https://huggingface.co/BricksDisplay/jevling-e2b-v0.1/resolve/main/system_one.json
164 Bytes
| { | |
| "system_one.readout": "letter_slot", | |
| "system_one.labels": "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz", | |
| "system_one.segment_separator": "\u001e" | |
| } |