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README.md

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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)