Instructions to use Tirth2109/qwen-indic-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Tirth2109/qwen-indic-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Tirth2109/qwen-indic-v1") 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
qwen-indic-v1
LoRA fine-tune of Qwen/Qwen3-Embedding-8B on Indic-language data using a 3-stage contrastive recipe with teacher preservation. Last-token pooling, L2-normalized embeddings.
- Base model: Qwen/Qwen3-Embedding-8B
- Pooling: last-token
- Languages: 22 scheduled Indic languages
- Training data: train splits only; decontaminated against MTEB test sets
- Downloads last month
- -