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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LimitedMouse/Generative-Embedding-Benchmark-Checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,092 Bytes
1aae1a7 | 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 35 | ---
license: apache-2.0
library_name: transformers
tags:
- generative-embedding
- multimodal
- benchmark
---
# GEB Decoder Checkpoints
Official decoder checkpoints for [Generative Embedding Benchmark](https://github.com/LimitedMouse/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:
```bash
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.
|