Text Ranking
Transformers
Safetensors
sentence-transformers
qwen3_vl
image-text-to-text
multimodal rerank
text rerank
Instructions to use ArchiveStudio/Qwen3-VL-Reranker-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArchiveStudio/Qwen3-VL-Reranker-8B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("ArchiveStudio/Qwen3-VL-Reranker-8B") model = AutoModelForMultimodalLM.from_pretrained("ArchiveStudio/Qwen3-VL-Reranker-8B", device_map="auto") - sentence-transformers
How to use ArchiveStudio/Qwen3-VL-Reranker-8B with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("ArchiveStudio/Qwen3-VL-Reranker-8B") query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Download modules.json from ArchiveStudio/Qwen3-VL-Reranker-8B: direct link, hf CLI and curl.
- Browser
- Download file 285 Bytes
-
https://huggingface.co/ArchiveStudio/Qwen3-VL-Reranker-8B/resolve/main/modules.json
- Command line
-
hf download hf://ArchiveStudio/Qwen3-VL-Reranker-8B/modules.json
-
curl -L -o modules.json https://huggingface.co/ArchiveStudio/Qwen3-VL-Reranker-8B/resolve/main/modules.json
285 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.base.modules.transformer.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_CausalScoreHead", | |
| "type": "sentence_transformers.cross_encoder.modules.logit_score.LogitScore" | |
| } | |
| ] |