Duplicated from google/embeddinggemma-2
How to use PulsePoint/Flex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="PulsePoint/Flex")
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("PulsePoint/Flex") model = AutoModel.from_pretrained("PulsePoint/Flex", device_map="auto")
How to use PulsePoint/Flex with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("PulsePoint/Flex") 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]
https://huggingface.co/PulsePoint/Flex/resolve/main/1_Pooling/config.json
hf download hf://PulsePoint/Flex/1_Pooling/config.json
curl -L -o config.json https://huggingface.co/PulsePoint/Flex/resolve/main/1_Pooling/config.json
