Duplicated from microsoft/harrier-oss-v1-0.6b
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]
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")
https://huggingface.co/furiosa-ai/harrier-oss-v1-0.6b/resolve/cb79ea5d02e67e947f0c6cd2e816d02c1982fe8c/vocab.json
hf download hf://furiosa-ai/harrier-oss-v1-0.6b@cb79ea5d02e67e947f0c6cd2e816d02c1982fe8c/vocab.json
curl -L -o vocab.json https://huggingface.co/furiosa-ai/harrier-oss-v1-0.6b/resolve/cb79ea5d02e67e947f0c6cd2e816d02c1982fe8c/vocab.json
