Feature Extraction
Model2Vec
Safetensors
sentence-transformers
Transformers
sentence-similarity
Qwen2
Instructions to use sarthak1/Qodo-Embed-M-1-1.5B-M2V-Distilled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Model2Vec
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") - sentence-transformers
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] - Transformers
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") - Notebooks
- Google Colab
- Kaggle
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": ".", | |
| "type": "sentence_transformers.models.StaticEmbedding" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Normalize", | |
| "type": "sentence_transformers.models.Normalize" | |
| } | |
| ] |