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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](https://github.com/ml-explore/mlx) weights for [Octen-Embedding-8B](https://huggingface.co/Octen/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](https://github.com/c-h-/octen-embeddings-server):
```bash
# 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)
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