---
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)