metadata
tags:
- mlx
- embeddings
- apple-silicon
- sentence-transformers
license: apache-2.0
base_model: Octen/Octen-Embedding-8B
library_name: mlx
Octen-Embedding-8B-mlx
Pre-converted MLX weights for Octen-Embedding-8B, ready to run on Apple Silicon.
Why this exists
The original model requires a ~30 minute conversion step and ~32GB temporary disk space. This repo provides the already-converted MLX weights so you can start embedding immediately.
Usage
With octen-embeddings-server:
# Clone the server
git clone https://github.com/c-h-/octen-embeddings-server.git
cd octen-embeddings-server
pip install -r requirements.txt
# Download pre-converted weights (instead of running convert_model.py)
huggingface-cli download chulcher/Octen-Embedding-8B-mlx --local-dir models/Octen-Embedding-8B-mlx
# Start the server
python3 server.py
The server exposes an OpenAI-compatible /v1/embeddings endpoint at http://localhost:8100.
Hardware Requirements
| Component | Requirement |
|---|---|
| CPU | Apple Silicon (M1/M2/M3/M4) |
| RAM | 20 GB+ |
| Disk | ~16 GB for weights |
| OS | macOS 13+ |
Performance
Octen-Embedding-8B ranks #1 on MTEB/RTEB with a score of 0.8045, outperforming commercial embedding APIs.
Typical latency on Apple Silicon: ~50-200ms per text depending on length.
License
Apache 2.0 (same as base model)