Feature Extraction
sentence-transformers
Safetensors
Transformers
gemma3_text
mteb
text-embeddings-inference
Instructions to use microsoft/harrier-oss-v1-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use microsoft/harrier-oss-v1-27b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("microsoft/harrier-oss-v1-27b") 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-v1-27b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="microsoft/harrier-oss-v1-27b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("microsoft/harrier-oss-v1-27b") model = AutoModel.from_pretrained("microsoft/harrier-oss-v1-27b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download 1_Pooling/config.json from microsoft/harrier-oss-v1-27b: direct link, hf CLI and curl.
- Browser
- Download file 297 Bytes
-
https://huggingface.co/microsoft/harrier-oss-v1-27b/resolve/main/1_Pooling/config.json
- Command line
-
hf download hf://microsoft/harrier-oss-v1-27b/1_Pooling/config.json
-
curl -L -o config.json https://huggingface.co/microsoft/harrier-oss-v1-27b/resolve/main/1_Pooling/config.json
297 Bytes
| { | |
| "word_embedding_dimension": 5376, | |
| "pooling_mode_cls_token": false, | |
| "pooling_mode_mean_tokens": false, | |
| "pooling_mode_max_tokens": false, | |
| "pooling_mode_mean_sqrt_len_tokens": false, | |
| "pooling_mode_weightedmean_tokens": false, | |
| "pooling_mode_lasttoken": true, | |
| "include_prompt": true | |
| } |