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", device_map="auto")# 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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Why distal?
#6
by breadlicker45 - opened
Couldn't you have just trained this one on the same dataset from the 27b? If anything it made this model worse and cost more to train.