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
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": null, | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "errors": "replace", | |
| "is_local": true, | |
| "model_max_length": 131072, | |
| "pad_token": "<|endoftext|>", | |
| "padding_side": "left", | |
| "split_special_tokens": false, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
| "tool_parser_type": "json_tools", | |
| "unk_token": null | |
| } | |