Instructions to use Qwen/Qwen3-Reranker-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen3-Reranker-0.6B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Reranker-0.6B") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Reranker-0.6B") - sentence-transformers
How to use Qwen/Qwen3-Reranker-0.6B with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("Qwen/Qwen3-Reranker-0.6B") 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
Qwen/Qwen3-Reranker-0.6B compatibility question
Dear [Developer/Team],
Thank you for your incredible contribution to the community with Qwen/Qwen3-Reranker-0.6B. It has been very helpful for my current project.
As I am planning to build upon this model, I would like to clarify its relationship with Qwen/Qwen3-0.6B-Base based on the model tree on Hugging Face:
Direct Fine-tuning: Is Qwen/Qwen3-Reranker-0.6B a direct fine-tuned version of Qwen/Qwen3-0.6B-Base, or were there intermediate models/checkpoints involved?
Inheritance: Does it strictly inherit the architecture and weights of Qwen/Qwen3-0.6B-Base without merging or distilling from other models?
This would help me ensure I use the model correctly.
Thank you for your time and support!