Feature Extraction
sentence-transformers
Safetensors
MLX
English
multilingual
qwen3
finance
legal
healthcare
code
stem
medical
mlx-my-repo
text-embeddings-inference
6-bit
Instructions to use lexrivera/zembed-1-embedding-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use lexrivera/zembed-1-embedding-mlx-6Bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("lexrivera/zembed-1-embedding-mlx-6Bit") 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] - MLX
How to use lexrivera/zembed-1-embedding-mlx-6Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir zembed-1-embedding-mlx-6Bit lexrivera/zembed-1-embedding-mlx-6Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| { | |
| "bos_token_id": 151643, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 151645, | |
| 151643 | |
| ], | |
| "pad_token_id": 151643, | |
| "temperature": 0.6, | |
| "top_k": 20, | |
| "top_p": 0.95, | |
| "transformers_version": "4.57.1" | |
| } | |