Feature Extraction
Transformers
Safetensors
sentence-transformers
qwen3
text-generation
text-embeddings-inference
text-ranking
reranker
int8
vllm
compressed-tensors
Instructions to use zankich/Qwen3-Reranker-0.6B-W8A16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zankich/Qwen3-Reranker-0.6B-W8A16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="zankich/Qwen3-Reranker-0.6B-W8A16")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zankich/Qwen3-Reranker-0.6B-W8A16") model = AutoModelForCausalLM.from_pretrained("zankich/Qwen3-Reranker-0.6B-W8A16", device_map="auto") - sentence-transformers
How to use zankich/Qwen3-Reranker-0.6B-W8A16 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("zankich/Qwen3-Reranker-0.6B-W8A16") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
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