Feature Extraction
Safetensors
sentence-transformers
Transformers
furiosa-llm
qwen3
furiosa-ai
harrier-oss-v1
mteb
text-embeddings-inference
Instructions to use furiosa-ai/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 furiosa-ai/harrier-oss-v1-0.6b with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("furiosa-ai/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 furiosa-ai/harrier-oss-v1-0.6b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="furiosa-ai/harrier-oss-v1-0.6b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("furiosa-ai/harrier-oss-v1-0.6b") model = AutoModel.from_pretrained("furiosa-ai/harrier-oss-v1-0.6b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 351 Bytes
cb79ea5 | 1 2 3 4 5 6 7 8 9 | {
"prompts": {
"web_search_query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: ",
"sts_query": "Instruct: Retrieve semantically similar text\nQuery: ",
"bitext_query": "Instruct: Retrieve parallel sentences\nQuery: "
},
"default_prompt_name": null,
"similarity_fn_name": "cosine"
} |