# Introduction
Deploy Encoder Transformers as self-contained, single-binary executables.
***
**Encoderfile** packages transformer encoders—and their classification heads—into a single, self-contained executable.
Replace fragile, multi-gigabyte Python containers with lean, auditable binaries that have **zero runtime dependencies**. Written in Rust and built on ONNX Runtime, Encoderfile ensures strict determinism and high performance for financial platforms, content moderation pipelines, and search infrastructure.
## Why Encoderfile?
While **Llamafile** focuses on generative models, **Encoderfile** is purpose-built for encoder architectures. It is designed for environments where compliance, latency, and determinism are non-negotiable.
* **Zero Dependencies:** No Python, no PyTorch, no network calls. Just a fast, portable binary.
* **Smaller Footprint:** Binaries are measured in megabytes, not the gigabytes required for standard container deployments.
* **Protocol Agnostic:** Runs as a REST API, gRPC microservice, CLI tool, or MCP Server out of the box.
* **Compliance-Friendly:** Deterministic and offline-safe, making it ideal for strict security boundaries.
> **Note for Windows users:** Pre-built binaries are not available for Windows. Please see our guide on [building from source](https://mozilla-ai.github.io/encoderfile/reference/building/) for instructions on building from source.
## Use Cases
| Scenario | Application |
| ------------------- | -------------------------------------------------------------------------------- |
| **Microservices** | Run as a standalone gRPC or REST service on localhost or in production. |
| **AI Agents** | Register as an MCP Server to give agents reliable classification tools. |
| **Batch Jobs** | Use the CLI mode (infer) to process text pipelines without spinning up servers. |
| **Edge Deployment** | Deploy sentiment analysis, NER, or embeddings anywhere without Docker or Python. |
## Supported Models
Encoderfile supports encoder-only transformers for:
* **Token Embeddings** - clustering, embeddings (BERT, DistilBERT, RoBERTa)
* **Sequence Classification** - Sentiment analysis, topic classification
* **Token Classification** - Named Entity Recognition, PII detection
* **Sentence Embeddings** - Semantic search, clustering
See our guide on [building from source](https://mozilla-ai.github.io/encoderfile/reference/building/) for detailed instructions on building the CLI tool from source.
Generation models (GPT, T5) are not supported. See [CLI Reference](/encoderfile/reference/cli.md) for complete model type details.
## Quick Start
### 1. Install CLI
Download the pre-built CLI tool:
```bash
curl -fsSL https://raw.githubusercontent.com/mozilla-ai/encoderfile/main/install.sh | sh
```
Or build from source (see [Building Guide](/encoderfile/reference/building.md)).
### 2. Export Model & Build
Export a HuggingFace model and build it into a binary:
```bash
# Export to ONNX
optimum-cli export onnx --model --task ./model
# Build the encoderfile
encoderfile build -f config.yml
```
See the [Building Guide](/encoderfile/reference/building.md) for detailed export options and configuration.
### 3. Run & Test
Start the server and make predictions:
```bash
# Start server
./build/sentiment-analyzer.encoderfile serve
# Make a prediction
curl -X POST http://localhost:8080/predict \
-H "Content-Type: application/json" \
-d '{"inputs": ["Your text here"]}'
```
See the [API Reference](/encoderfile/reference/api-reference.md) for complete endpoint documentation.
**Next Steps:** Try the [Token Classification Cookbook](/encoderfile/cookbooks/token-classification-ner.md) for a complete walkthrough.
## How It Works
Encoderfile compiles your model into a self-contained binary by embedding ONNX weights, tokenizer, and config directly into Rust code. The result is a portable executable with zero runtime dependencies.
## Documentation
### Getting Started
* [**Installation & Setup**](/encoderfile/getting-started.md) - Complete setup guide from installation to first deployment
* [**Building Guide**](/encoderfile/reference/building.md) - Export models and configure builds
### Tutorials
* [**Token Classification (NER)**](/encoderfile/cookbooks/token-classification-ner.md) - Build a Named Entity Recognition system
* [**Transforms Guide**](/encoderfile/transforms/index.md) - Custom post-processing with Lua scripts
### Python Library
* [**Building with Python**](/encoderfile/python-library/building-with-python.md) - Build encoderfiles programmatically with the Python package
* [**Python API Reference**](/encoderfile/python-library/api-reference.md) - Complete reference for all classes and functions
### Reference
* [**CLI Reference**](/encoderfile/reference/cli.md) - Full documentation for `build`, `serve`, and `infer` commands
* [**API Reference**](/encoderfile/reference/api-reference.md) - REST, gRPC, and MCP endpoint specifications
## Community & Support
* [**GitHub Issues**](https://github.com/mozilla-ai/encoderfile/issues) - Report bugs or request features
* [**Contributing Guide**](/encoderfile/community/contributing.md) - Learn how to contribute
* [**Code of Conduct**](/encoderfile/community/code_of_conduct.md) - Community guidelines
{% hint style="info" %}
Standard builds of Encoderfile require glibc to run because of the ONNX runtime. See [this issue](https://github.com/mozilla-ai/encoderfile/issues/69) on progress on building Encoderfile for musl linux.
{% endhint %}
---
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