Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
sentence-similarity
text-embeddings-inference
Instructions to use ddriscoll/stingray-dense-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ddriscoll/stingray-dense-LoRA with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ddriscoll/stingray-dense-LoRA") 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] - Transformers
How to use ddriscoll/stingray-dense-LoRA with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ddriscoll/stingray-dense-LoRA")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ddriscoll/stingray-dense-LoRA") model = AutoModel.from_pretrained("ddriscoll/stingray-dense-LoRA") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18116509be2d939a4102b39994e55d8e118cdd861a5579f25def76c6def9dd76
- Size of remote file:
- 11.4 MB
- SHA256:
- def6d7e5dbd6a836e040d2a4ed49bda604e3e87a48255a3de32dd2c1d3b86c96
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