Instructions to use apus-ailab/APUS-OpenJev-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apus-ailab/APUS-OpenJev-v1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("apus-ailab/APUS-OpenJev-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,941 Bytes
68e5880 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | # APUS-OpenJev-v1 路 9B Research Variant
A decision model for browser agents and business workflows. This directory contains standalone BF16 weights and a runtime with selectable `effort="low"` and `effort="high"` compute budgets.
[Model family](../README.md) 路 [Architecture](../ARCHITECTURE.md) 路 [Runtime guide](RUNTIME.md)
## Quick start
Use a CUDA-capable PyTorch environment.
```bash
python -m pip install huggingface_hub
hf auth login
hf download apus-ailab/APUS-OpenJev-v1 \
--include "9B-5949/*" --local-dir ./APUS-OpenJev-v1
cd ./APUS-OpenJev-v1/9B-5949
python -m pip install -r requirements.txt
python examples.py . --device cuda:0 --effort high
```
The included runtime provides compute-budget selection. Use `high` for text generation.
## Evaluation
With the full compute budget, this merged model scores **67/80 (83.75%)** on the [Frozen80 development panel](https://huggingface.co/datasets/gump2049/xDAN-openJet-Eval-Frozen80-20260921): browser action selection, principle-based judgment, evidence-based questions, natural language inference, and attribute decisions.
This reused development panel is an engineering reference, not an independent benchmark. BF16 merging changes some candidate probabilities; decision thresholds require revalidation. See [evaluation results](merged-evaluation.json) and [runtime checks](evaluation/runtime-smoke.json) for details.
## Provenance
We thank the Qwen team for the [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) base model. Training details and source records are in [training.md](training.md); artifact hashes are in [release-manifest.json](release-manifest.json). Consult [LICENSE](LICENSE) and the [base-model](provenance/BASE-LICENSE.txt) and [source-project](provenance/SOURCE-PROJECT-LICENSE.txt) notices.
**Authors:** gumpcheng ([https://huggingface.co/xDAN2099](https://huggingface.co/xDAN2099)), zhangxu, [APUS AI-LAB](https://github.com/APUS-AI-Lab)
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