Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
mteb
text-embeddings-inference
Instructions to use microsoft/harrier-oss-v1-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use microsoft/harrier-oss-v1-0.6b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("microsoft/harrier-oss-v1-0.6b") 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 microsoft/harrier-oss-v1-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/harrier-oss-v1-0.6b")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/harrier-oss-v1-0.6b") model = AutoModel.from_pretrained("microsoft/harrier-oss-v1-0.6b") - Inference
- Notebooks
- Google Colab
- Kaggle
Reranker model
#7
by Duonglv - opened
Hello Microsoft team,
This is a great embedding model with only 0.6B params. It is stronger than many other models with similar size, especially qwen3 embedding 0.6B.
However, I wish that you guys will release a reranker 0.6B model as well. It’s very great if we use both embedding and reranker harrier-oss models togerther.
Beside that, running a big model needs much gpu vRam with very expensive cost, so it would be great if you guys can release variety model sizes, such as 0.3B, 0.6B, 1B, 2B, 4B,…
Thanks alot.