Building Encoderfiles from Source

This guide explains how to build custom encoderfile binaries from HuggingFace transformer models.

Prerequisites

Before building encoderfiles, ensure you have:

• Python 3.13+ - For exporting models to ONNX
• uv - Python package manager

If you are compiling the encoderfile CLI from source, make sure you also have:

• Rust - For building the CLI tool and binaries
• protoc - Protocol Buffer compiler

To compile encoderfile's Python bindings, you must also have Maturin installed. Instructions to install Maturin can be found here.

Installing Prerequisites

macOS:

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install protoc
brew install protobuf

Linux:

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Install uv
curl -LsSf https://astral.sh/uv/install.sh | sh

# Install protoc (Ubuntu/Debian)
sudo apt-get install protobuf-compiler

# Install protoc (Fedora)
sudo dnf install protobuf-compiler

Windows:

# Install Rust - Download rustup-init.exe from https://rustup.rs

# Install uv
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# Install protoc - Download from https://github.com/protocolbuffers/protobuf/releases

Development Setup

If you're contributing to encoderfile or modifying the source:

# Clone the repository
git clone https://github.com/mozilla-ai/encoderfile.git
cd encoderfile

# Set up the development environment
just setup

This will: - Install Rust dependencies - Create a Python virtual environment - Download model weights for integration tests

Building the CLI Tool

First, build the encoderfile CLI tool:

cargo build --bin encoderfile --release

The CLI binary will be created at ./target/release/encoderfile.

Optionally, install it to your system:

cargo install --path encoderfile --bin encoderfile

Step-by-Step: Building an Encoderfile

Step 1: Prepare Your Model

You need a HuggingFace model with ONNX weights. You can either export a model or use one with existing ONNX weights.

Option A: Export a Model to ONNX

Use optimum-cli to export any HuggingFace model:

optimum-cli export onnx \
  --model <model_id> \
  --task <task_type> \
  <output_directory>

Examples:

Embedding model:

optimum-cli export onnx \
  --model sentence-transformers/all-MiniLM-L6-v2 \
  --task feature-extraction \
  ./models/embedder

Sentiment classifier:

optimum-cli export onnx \
  --model distilbert-base-uncased-finetuned-sst-2-english \
  --task text-classification \
  ./models/sentiment

NER model:

optimum-cli export onnx \
  --model dslim/bert-base-NER \
  --task token-classification \
  ./models/ner

Available task types: - feature-extraction - For embedding models - text-classification - For sequence classification - token-classification - For token classification (NER, POS tagging, etc.)

See the HuggingFace task guide for more options.

Option B: Use a Pre-Exported Model

Some models on HuggingFace already have ONNX weights:

git clone https://huggingface.co/optimum/distilbert-base-uncased-finetuned-sst-2-english

Verify Model Structure

Your model directory should contain:

my_model/
├── config.json          # Model configuration
├── model.onnx           # ONNX weights (required)
├── tokenizer.json       # Tokenizer (required)
├── special_tokens_map.json
├── tokenizer_config.json
└── vocab.txt

Step 2: Create Configuration File

Create a YAML configuration file (e.g., config.yml):

encoderfile:
  # Model identifier (used in API responses)
  name: my-model

  # Model version (optional, defaults to "0.1.0")
  version: "1.0.0"

  # Path to model directory
  path: ./models/my-model

  # Model type: embedding, sequence_classification, or token_classification
  model_type: embedding

  # Output path (optional, defaults to ./<name>.encoderfile in current directory)
  output_path: ./build/my-model.encoderfile

  # Cache directory (optional, defaults to system cache)
  cache_dir: ~/.cache/encoderfile

  # Optional: Lua transform for post-processing
  # transform:
  #   path: ./transforms/normalize.lua

Alternative: Specify file paths explicitly:

encoderfile:
  name: my-model
  model_type: embedding
  output_path: ./build/my-model.encoderfile
  path:
    model_config_path: ./models/config.json
    model_weights_path: ./models/model.onnx
    tokenizer_path: ./models/tokenizer.json

Step 3: Build the Encoderfile

Build your encoderfile binary:

./target/release/encoderfile build -f config.yml

Or, if you installed the CLI:

encoderfile build -f config.yml

The build process will:

1. Detect your system platform and download the base runtime binary
2. Load and validate the configuration
3. Check for required model files
4. Validate the ONNX model structure
5. Format assets and append to the base binary
6. Output the binary to the specified path (or ./<name>.encoderfile if not specified)

For more information on encoderfile file formats and build process, check out our page on Encoderfile File Format.

Build output:

./build/my-model.encoderfile

Step 4: Test Your Encoderfile

Make the binary executable and test it:

chmod +x ./build/my-model.encoderfile

# Test with CLI inference
./build/my-model.encoderfile infer "Test input"

# Or start the server
./build/my-model.encoderfile serve

Configuration Options

For a complete set of configuration options, see the CLI Reference

Model Types

Embedding Models

For models using AutoModel or AutoModelForMaskedLM:

encoderfile:
  name: my-embedder
  path: ./models/embedding-model
  model_type: embedding
  output_path: ./build/my-embedder.encoderfile

Examples: - bert-base-uncased - distilbert-base-uncased - sentence-transformers/all-MiniLM-L6-v2

Sequence Classification Models

For models using AutoModelForSequenceClassification:

encoderfile:
  name: my-classifier
  path: ./models/classifier-model
  model_type: sequence_classification
  output_path: ./build/my-classifier.encoderfile

Examples: - distilbert-base-uncased-finetuned-sst-2-english (sentiment) - roberta-large-mnli (natural language inference) - facebook/bart-large-mnli (entailment)

Token Classification Models

For models using AutoModelForTokenClassification:

encoderfile:
  name: my-ner
  path: ./models/ner-model
  model_type: token_classification
  output_path: ./build/my-ner.encoderfile

Examples: - dslim/bert-base-NER - bert-base-cased-finetuned-conll03-english - dbmdz/bert-large-cased-finetuned-conll03-english

Advanced Features

Cross-compilation

Specify a target architecture for your encoderfile by using the --platform argument:

encoderfile build -f encoderfile.yml --platform <insert_target_here>

Encoderfile releases pre-built base binaries for the following architectures:

• x86_64-unknown-linux-gnu
• aarch64-unknown-linux-gnu
• x86_64-apple-darwin
• aarch64-apple-darwin

If you want to build the base binary locally, you can also point to a path. For example:

# build encoderfile base binary from source (will be at ./target/release/encoderfile-runtime)
cargo build -p encoderfile-runtime --release

# create encoderfile
encoderfile build \
  -f encoderfile.yml \
  --base-binary-path ./target/release/encoderfile-runtime

Lua Transforms

Add custom post-processing with Lua scripts:

encoderfile:
  name: my-model
  path: ./models/my-model
  model_type: token_classification
  transform:
    path: ./transforms/softmax_logits.lua

Inline transform:

encoderfile:
  name: my-model
  path: ./models/my-model
  model_type: embedding
  transform: "return lp_normalize(output)"

By default, libraries table, string and math are enabled if property lua_libs is not present. This property allows you to specify a different set of libraries as strings, to choose from:

• coroutine
• table
• io
• os
• string
• utf8
• math
• package

Note that, if this property is present, no libraries are loaded by default, so all used libraries must be present.

Inline transform:

encoderfile:
  name: my-model
  path: ./models/my-model
  model_type: embedding
  lua_libs:
    - table
    - string
    - math
    - os
  transform: | 
    t = os.time()
    return lp_normalize(output)

Custom Cache Directory

Specify a custom cache location:

encoderfile:
  name: my-model
  path: ./models/my-model
  model_type: embedding
  cache_dir: /tmp/encoderfile-cache

Troubleshooting

Error: "No such file: model.onnx"

Solution: Ensure your model directory contains ONNX weights.

# Export with optimum-cli
optimum-cli export onnx --model <model_id> --task <task> <output_dir>

Error: "Could not locate model config at path"

Solution: The model directory is missing required files (config.json, tokenizer.json, model.onnx).

# Check directory contents
ls -la ./path/to/model

Error: "cargo build failed"

Solution: Check that Rust and dependencies are installed.

rustc --version
cargo --version
protoc --version

Build is very slow

Solution: The first build compiles many dependencies. Subsequent builds will be faster. Use release mode for production:

# Debug builds are slow
cargo build --bin encoderfile

# Release builds are optimized
cargo build --bin encoderfile --release

CI/CD Integration

GitHub Actions Example

name: Build Encoderfile

on:
  push:
    branches: [main]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: Install Rust
        uses: dtolnay/rust-toolchain@stable

      - name: Install protoc
        run: sudo apt-get install -y protobuf-compiler

      - name: Export model to ONNX
        run: |
          pip install optimum[exporters]
          optimum-cli export onnx \
            --model distilbert-base-uncased \
            --task feature-extraction \
            ./model

      - name: Create config
        run: |
          cat > config.yml <<EOF
          encoderfile:
            name: my-model
            path: ./model
            model_type: embedding
            output_path: ./build/my-model.encoderfile
          EOF

      - name: Build encoderfile
        run: |
          cargo build --bin encoderfile --release
          ./target/release/encoderfile build -f config.yml

      - uses: actions/upload-artifact@v3
        with:
          name: encoderfile
          path: ./build/*.encoderfile

Binary Distribution

After building, your encoderfile binary is completely self-contained:

• No Python runtime required
• No external dependencies
• No network calls needed
• Portable across systems with the same architecture

You can distribute the binary by:

1. Copying it to the target system
2. Making it executable: chmod +x my-model.encoderfile
3. Running it: ./my-model.encoderfile serve

Next Steps

• CLI Reference - Complete command-line documentation
• API Reference - REST, gRPC, and MCP APIs
• Getting Started Guide - Step-by-step tutorial
• Contributing - Help improve encoderfile