Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
text-generation
sentence-similarity
text-embeddings-inference
Instructions to use fabianschmidt-cohere/Qwen3-Embedding-2.4M-init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use fabianschmidt-cohere/Qwen3-Embedding-2.4M-init with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("fabianschmidt-cohere/Qwen3-Embedding-2.4M-init") 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 fabianschmidt-cohere/Qwen3-Embedding-2.4M-init with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="fabianschmidt-cohere/Qwen3-Embedding-2.4M-init")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fabianschmidt-cohere/Qwen3-Embedding-2.4M-init") model = AutoModelForCausalLM.from_pretrained("fabianschmidt-cohere/Qwen3-Embedding-2.4M-init") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e1c5ae75d50677cf0cce3ad0441fbf25396122838224b9473c03004e92077538
- Size of remote file:
- 11.4 MB
- SHA256:
- def76fb086971c7867b829c23a26261e38d9d74e02139253b38aeb9df8b4b50a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.