Text Classification
Transformers
Safetensors
English
qwen3_5
image-text-to-text
decision-model
jev
nimble
qwen3.5
calibration
negative-control
structured-prediction
Instructions to use richardyoung/Bev-9B-inverted with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use richardyoung/Bev-9B-inverted with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="richardyoung/Bev-9B-inverted")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("richardyoung/Bev-9B-inverted") model = AutoModelForMultimodalLM.from_pretrained("richardyoung/Bev-9B-inverted", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 122 Bytes
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