Instructions to use LimitedMouse/Generative-Embedding-Benchmark-Checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LimitedMouse/Generative-Embedding-Benchmark-Checkpoints with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LimitedMouse/Generative-Embedding-Benchmark-Checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
metadata
license: apache-2.0
library_name: transformers
tags:
- generative-embedding
- multimodal
- benchmark
GEB Decoder Checkpoints
Official decoder checkpoints for Generative Embedding Benchmark.
| Embedder | Mode | Subfolder |
|---|---|---|
| Qwen3-VL-Embedding-2B | Visual-only | qwen3vl-2b/visual_only |
| Qwen3-VL-Embedding-2B | VL-joint | qwen3vl-2b/vl_joint |
| Qwen3-VL-Embedding-8B | Visual-only | qwen3vl-8b/visual_only |
| Qwen3-VL-Embedding-8B | VL-joint | qwen3vl-8b/vl_joint |
Each checkpoint contains the trained Qwen3-0.6B readout and its embedding adapter. The embedding model itself is not bundled. Use the matching embedder and evaluation mode shown above.
Install and evaluate through the code repository:
pip install -e '.[eval]'
NPROC=8 BATCH_SIZE=16 bash scripts/evaluate_paper.sh \
qwen3vl-2b auto test visual_only
The checkpoints are intended for reproducing the GEB paper results. The Qwen models and upstream evaluation datasets remain subject to their own licenses.