Feature Extraction
Transformers
Safetensors
qwen3
tevatron
tevatron-elastic
information-retrieval
reranker
elastic
text-embeddings-inference
Instructions to use utahnlp/tevatron-elastic-qwen3-0.6b-reranker-depth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use utahnlp/tevatron-elastic-qwen3-0.6b-reranker-depth with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="utahnlp/tevatron-elastic-qwen3-0.6b-reranker-depth")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("utahnlp/tevatron-elastic-qwen3-0.6b-reranker-depth") model = AutoModel.from_pretrained("utahnlp/tevatron-elastic-qwen3-0.6b-reranker-depth", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7df2f04f393f55b30b0633994f2312b706af0fa34dc9f1f451f9afc6448129d4
- Size of remote file:
- 32.1 kB
- SHA256:
- f4905d25f73b531c9c583bb90da44cf169a7c34cebc96fb223e688f42ebef20a
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