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
Download art/bev-benchmarks.jpg from richardyoung/Bev-9B-inverted: direct link, hf CLI and curl.
- Browser
- Download file 400 kB
-
https://huggingface.co/richardyoung/Bev-9B-inverted/resolve/main/art/bev-benchmarks.jpg
- Command line
-
hf download hf://richardyoung/Bev-9B-inverted/art/bev-benchmarks.jpg
-
curl -L -o bev-benchmarks.jpg https://huggingface.co/richardyoung/Bev-9B-inverted/resolve/main/art/bev-benchmarks.jpg
400 kB

- Xet hash:
- f6647b23dd3f0f2ecf298b0b65ed867afab97c0aedf4f046c640116e506e8a77
- Size of remote file:
- 400 kB
- SHA256:
- e4a9b25c2f412ef8e576d47ab7c8f0d203d739bd24118af0f71dba905dcc13c2
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