metadata
license: cc-by-nc-4.0
language:
- en
- multilingual
base_model: zeroentropy/zembed-1-embedding
pipeline_tag: feature-extraction
tags:
- finance
- legal
- healthcare
- code
- stem
- medical
- multilingual
- mlx
- mlx-my-repo
library_name: sentence-transformers
model_max_length: 32768
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)