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 generation_config.json from BricksDisplay/jevling-e2b-v0.1: direct link, hf CLI and curl.
- Browser
- Download file 204 Bytes
-
https://huggingface.co/BricksDisplay/jevling-e2b-v0.1/resolve/main/generation_config.json
- Command line
-
hf download hf://BricksDisplay/jevling-e2b-v0.1/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/BricksDisplay/jevling-e2b-v0.1/resolve/main/generation_config.json
204 Bytes
| { | |
| "bos_token_id": 2, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 1, | |
| 106, | |
| 50 | |
| ], | |
| "pad_token_id": 0, | |
| "temperature": 1.0, | |
| "top_k": 64, | |
| "top_p": 0.95, | |
| "transformers_version": "5.17.0" | |
| } | |