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: 1,060 Bytes
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Copyright 2026 Richard Young
Released under the Apache License, Version 2.0 (see LICENSE).
This release is built on, and includes material from, the following works:
- Qwen3.5-9B by the Qwen team, Apache License 2.0. The model weights in this release are Qwen3.5-9B with a
further-trained adapter merged in. https://huggingface.co/Qwen/Qwen3.5-9B
- Bespoke-Nimble-9B-v2 by Bespoke Labs, Apache License 2.0. Its LoRA adapter was the starting point for
training. https://huggingface.co/bespokelabs/Bespoke-Nimble-9B-v2
- Bespoke-Nimble-9B by Bespoke Labs, Apache License 2.0. Its prompt contract is included unmodified:
parallel_schema.py, extended_schema.py, serving_schema.py, candidate_schema.py, schema_config.json,
serving_config.json. https://huggingface.co/bespokelabs/Bespoke-Nimble-9B
The training rows come from Bespoke Labs' public Nimble repository (https://github.com/bespokelabsai/nimble),
which does not state a license. They are not included in this release.
Bev is not affiliated with TypeSafe AI or Bespoke Labs.
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