Feature Extraction
sentence-transformers
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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
lexrivera/zembed-1-embedding-mlx-6Bit
The Model lexrivera/zembed-1-embedding-mlx-6Bit was converted to MLX format from zeroentropy/zembed-1-embedding using mlx-lm version 0.31.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("lexrivera/zembed-1-embedding-mlx-6Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
0.9B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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6-bit