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] hf download hfadam/WeMM-Embedding-2B-MLX-4bit --local-dir 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
Download modules.json from hfadam/WeMM-Embedding-2B-MLX-4bit: direct link, hf CLI and curl.
- Browser
- Download file 106 Bytes
-
https://huggingface.co/hfadam/WeMM-Embedding-2B-MLX-4bit/resolve/main/modules.json
- Command line
-
hf download hf://hfadam/WeMM-Embedding-2B-MLX-4bit/modules.json
-
curl -L -o modules.json https://huggingface.co/hfadam/WeMM-Embedding-2B-MLX-4bit/resolve/main/modules.json
106 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "modeling_st_wemm.WeMMTransformer" | |
| } | |
| ] | |