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:
- abc5817ab40a78914fc749f8a808be7e4389f9e3879f03bffc2234b3c4c4bd1f
- Size of remote file:
- 1.19 GB
- SHA256:
- bb976f611d590ef4b132898280ef4659d1cfdfa7a754f33f813391fc91542f6e
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