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---
tags:
- encoderfile
---
# Encoderfile Models

**IMPORTANT NOTE: This repository is now deprecated!** Please check out our new [`encoderfiles` collection](https://huggingface.co/collections/mozilla-ai/encoderfiles) to find your favorite prebuilt encoderfiles.

---

Pre-built [encoderfiles](https://github.com/mozilla-ai/encoderfile) for popular Hugging Face embedding models — self-contained executables that run as embedding servers with no Python or ML dependencies required.

## Available Models

| Model | Details |
|---|---|
| [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2/) | 384-dim, English sentence embeddings |
| [deepset/deberta-v3-base-injection](https://huggingface.co/deepset/deberta-v3-base-injection) | DeBERTa-variant prompt injection classification model |
| [JasperLS/deberta-v3-base-injection](https://huggingface.co/JasperLS/deberta-v3-base-injection) | DeBERTa-variant prompt injection classification model |
| [JasperLS/gelectra-base-injection](https://huggingface.co/JasperLS/gelectra-base-injection) | gELECTRA-variant prompt injection classification model |
| [protectai/deberta-v3-base-prompt-injection-v2](https://huggingface.co/protectai/deberta-v3-base-prompt-injection-v2) | DeBERTa-variant prompt injection classification model |
| [protectai/deberta-v3-small-prompt-injection-v2](https://huggingface.co/protectai/deberta-v3-small-prompt-injection-v2) | DeBERTa-variant prompt injection classification model |
| [protectai/deberta-v3-base-prompt-injection](https://huggingface.co/protectai/deberta-v3-base-prompt-injection) | DeBERTa-variant prompt injection classification model |
| [protectai/distilroberta-base-rejection-v1](https://huggingface.co/protectai/distilroberta-base-rejection-v1) | DistilRoBERTa-variant prompt injection classification model |
| [DuoGuard/DuoGuard-0.5B](https://huggingface.co/DuoGuard/DuoGuard-0.5B) | Content moderation model based on Qwen2.5-0.5B |

More models coming soon.

## Usage

Each model directory contains platform-specific binaries. Download the one for your platform, make it executable, and run:

```bash
# Serve embeddings over HTTP
./all-MiniLM-L6-v2.aarch64-apple-darwin.encoderfile serve

# Or infer directly from the CLI
./all-MiniLM-L6-v2.aarch64-apple-darwin.encoderfile infer "this is a test"
```

If you don't see the model you want or are using an exotic architecture, check out our guide on [Building encoderfiles](https://mozilla-ai.github.io/encoderfile/reference/building/).

## About

These encoderfiles are built and published by [mozilla-ai](https://huggingface.co/mozilla-ai) using the [Encoderfile](https://github.com/mozilla-ai/encoderfile) tool. To build your own, see the [Encoderfile documentation](https://mozilla-ai.github.io/encoderfile).