Feature Extraction
MLX
Safetensors
sentence-transformers
Chinese
English
qwen3_5
multimodal-embedding
text-embedding
image-embedding
video-embedding
mrl
custom_code
4-bit precision
Instructions to use hfadam/WeMM-Embedding-2B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use hfadam/WeMM-Embedding-2B-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir WeMM-Embedding-2B-MLX-4bit hfadam/WeMM-Embedding-2B-MLX-4bit
- sentence-transformers
How to use hfadam/WeMM-Embedding-2B-MLX-4bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hfadam/WeMM-Embedding-2B-MLX-4bit", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Model size
0.7B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit