Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen3
bnb-my-repo
sentence-similarity
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use lainlives/Qwen3-Embedding-4B-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lainlives/Qwen3-Embedding-4B-bnb-4bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lainlives/Qwen3-Embedding-4B-bnb-4bit") 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 lainlives/Qwen3-Embedding-4B-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="lainlives/Qwen3-Embedding-4B-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("lainlives/Qwen3-Embedding-4B-bnb-4bit") model = AutoModelForMultimodalLM.from_pretrained("lainlives/Qwen3-Embedding-4B-bnb-4bit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- fe0078304f47f27ce763434a2142283cb7abeff9e34e5153cad673f4ab1287b6
- Size of remote file:
- 2.65 GB
- SHA256:
- 7fda85652dc748f3f494a2b6bd06894dc77d7c0e70a279d3d9c9ce61377649e7
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