How to use sarthak1/Qodo-Embed-M-1-1.5B-M2V-Distilled with Model2Vec:
from model2vec import StaticModel model = StaticModel.from_pretrained("sarthak1/Qodo-Embed-M-1-1.5B-M2V-Distilled")
How to use sarthak1/Qodo-Embed-M-1-1.5B-M2V-Distilled with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("sarthak1/Qodo-Embed-M-1-1.5B-M2V-Distilled") 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 sarthak1/Qodo-Embed-M-1-1.5B-M2V-Distilled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="sarthak1/Qodo-Embed-M-1-1.5B-M2V-Distilled")
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("sarthak1/Qodo-Embed-M-1-1.5B-M2V-Distilled", device_map="auto")
