CLI Reference

Overview

Encoderfile provides two command-line tools:

1. cli - Rust-based build tool for creating encoderfile binaries from ONNX models
2. encoderfile - Rust-based runtime binary for serving models and running inference

Build Tool: cli

The cli build command compiles HuggingFace transformer models (with ONNX weights) into self-contained executable binaries using a YAML configuration file.

build

Validates a model configuration and builds a self-contained Rust binary with embedded model assets.

Usage

# If you haven't installed the CLI tool yet, build it first:
cargo build --bin encoderfile --release

# Then run it:
./target/release/encoderfile build -f <config.yml> [OPTIONS]

# Or install it to your system:
cargo install --path encoderfile --bin encoderfile
encoderfile build -f <config.yml> [OPTIONS]

Options

Option	Short	Type	Required	Description
-	-f	Path	Yes	Path to YAML configuration file
--output-dir	-	Path	No	Override output directory from config
--cache-dir	-	Path	No	Override cache directory from config
--no-build	-	Flag	No	Generate project files without building
--base-binary-path	-	Path	No	Specify custom local base binary
--platform	-	Option	No	Target platform for compiled binary (e.g., aarch64-apple-darwin, x86_64-unknown-linux-gnu). Equivalent of Cargo's --target. Default is the architecture of whatever machine you are using.
--runtime-version	-	Option	No	Override default encoderfile runtime version
--no-download	-	Flag	No	Disable downloading of base binary

Configuration File Format

Create a YAML configuration file (e.g., config.yml) with the following structure:

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 or explicit file paths
  path: ./models/my-model
  # OR specify files explicitly:
  # path:
  #   model_config_path: ./models/config.json
  #   model_weights_path: ./models/model.onnx
  #   tokenizer_path: ./models/tokenizer.json

  # 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 transform (Lua script for post-processing)
  transform:
    path: ./transforms/normalize.lua
  # OR inline transform:
  # transform: "function Postprocess(logits) return logits:lp_normalize(2.0, 2.0) end"

  # Whether to validate transform with a dry-run (optional, defaults to true)
  validate_transform: true

  # Whether to build the binary (optional, defaults to true)
  build: true

Model Types

• embedding - For models using AutoModel or AutoModelForMaskedLM

  • Outputs: last_hidden_state with shape [batch_size, sequence_length, hidden_size]
• sequence_classification - For models using AutoModelForSequenceClassification

  • Outputs: logits with shape [batch_size, num_labels]
• token_classification - For models using AutoModelForTokenClassification

  • Outputs: logits with shape [batch_size, num_tokens, num_labels]

Examples

Build an embedding model:

Create embedding-config.yml:

encoderfile:
  name: sentence-embedder
  version: "1.0.0"
  path: ./models/all-MiniLM-L6-v2
  model_type: embedding
  output_path: ./build/sentence-embedder.encoderfile

Build:

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

Build a sentiment classifier:

Create sentiment-config.yml:

encoderfile:
  name: sentiment-analyzer
  path: ./models/distilbert-sst2
  model_type: sequence_classification

Build:

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

Build a NER model with transform:

Create ner-config.yml:

encoderfile:
  name: ner-tagger
  path: ./models/bert-ner
  model_type: token_classification
  transform:
    path: ./transforms/softmax_logits.lua

Build:

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

Generate without building:

./target/release/encoderfile build -f config.yml --no-build

Override output directory:

./target/release/encoderfile build -f config.yml --output-dir ./custom-output

Build Process

The build command performs the following steps:

1. Loads configuration - Parses the YAML config file
2. Validates model files - Checks for required files:

   • model.onnx - ONNX model weights (or path specified in config)
   • tokenizer.json - Tokenizer configuration (or path specified in config)
   • config.json - Model configuration (or path specified in config)
3. Validates ONNX model - Checks the ONNX model structure and compatibility
4. Embeds assets - Appends embedded artifacts to a pre-built base binary
5. Outputs binary - Copies the binary to the specified output path

Output

Upon successful build, you'll find the binary at the path specified in output_path.

If output_path is not specified, the binary defaults to:

./<name>.encoderfile

For example, with name: my-model and output_path: ./build/my-model.encoderfile:

./build/my-model.encoderfile

This binary is completely self-contained and includes:

• ONNX model weights (embedded at compile time)
• Tokenizer configuration (embedded)
• Model metadata (embedded)
• Full inference runtime

Requirements

Before building, ensure you have:

• Valid ONNX model files

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

• Rust toolchain
• protoc Protocol Buffer compiler

Troubleshooting

Error: "No such file: model.onnx"

Solution: Ensure your model directory contains ONNX weights.
Export with: 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.
Ensure the directory contains: config.json, tokenizer.json, and model.onnx

Error: "No such directory"

Solution: The path specified in the config file doesn't exist.
Check the path value in your YAML config.

Error: "Cannot locate cache directory"

Solution: System cannot determine the cache directory.
Specify an explicit cache_dir in your config file.

---

version

Prints the encoderfile version.

Usage

./target/release/encoderfile version

Output

Encoderfile 0.1.0

---

Runtime Binary: encoderfile

After building with the cli tool, the resulting .encoderfile binary provides inference capabilities.

Architecture

The runtime CLI is built with the following components:

• Server Mode: Hosts models via HTTP and/or gRPC endpoints
• Inference Mode: Performs one-off inference operations from the command line
• Multi-Model Support: Automatically detects and routes to the appropriate model type

Commands

serve

Starts the encoderfile server with HTTP and/or gRPC endpoints for model inference.

Usage

encoderfile serve [OPTIONS]

Options

Option	Type	Default	Description
--grpc-hostname	String	[::]	Hostname/IP address for the gRPC server
--grpc-port	String	50051	Port for the gRPC server
--http-hostname	String	0.0.0.0	Hostname/IP address for the HTTP server
--http-port	String	8080	Port for the HTTP server
--disable-grpc	Boolean	false	Disable the gRPC server
--disable-http	Boolean	false	Disable the HTTP server

Examples

Start both HTTP and gRPC servers (default):

encoderfile serve

Start only HTTP server:

encoderfile serve --disable-grpc

Start only gRPC server:

encoderfile serve --disable-http

Custom ports and hostnames:

encoderfile serve \
  --http-hostname 127.0.0.1 \
  --http-port 3000 \
  --grpc-hostname localhost \
  --grpc-port 50052

Notes

• At least one server type (HTTP or gRPC) must be enabled
• The server will display a banner upon successful startup
• Both servers run concurrently using async tasks

---

infer

Performs inference on input text using the configured model. The model type is automatically detected based on configuration.

Usage

encoderfile infer <INPUTS>... [OPTIONS]

Arguments

Argument	Required	Description
<INPUTS>	Yes	One or more text strings to process

Options

Option	Type	Default	Description
-f, --format	Enum	json	Output format (currently only JSON is supported)
-o, --out-dir	String	None	Output file path; if not provided, prints to stdout

Model Types

The inference behavior depends on the model type configured:

1. Embedding Models

Generates vector embeddings for input text.

Example:

encoderfile infer "Hello world" "Another sentence"

With normalization disabled:

encoderfile infer "Hello world" --normalize=false

2. Sequence Classification Models

Classifies entire sequences (e.g., sentiment analysis, topic classification).

Example:

encoderfile infer "This product is amazing!" "I'm very disappointed"

3. Token Classification Models

Labels individual tokens (e.g., Named Entity Recognition, Part-of-Speech tagging).

Example:

encoderfile infer "Apple Inc. is located in Cupertino, California"

Output Formats

Currently, only JSON format is supported (--format json). The output structure varies by model type:

Embedding Output

{
  "embeddings": [
    [0.123, -0.456, 0.789, ...],
    [0.321, -0.654, 0.987, ...]
  ],
  "metadata": null
}

Sequence Classification Output

{
  "predictions": [
    {
      "label": "POSITIVE",
      "score": 0.9876
    },
    {
      "label": "NEGATIVE",
      "score": 0.8765
    }
  ],
  "metadata": null
}

Token Classification Output

{
  "predictions": [
    {
      "tokens": ["Apple", "Inc.", "is", "located", "in", "Cupertino", ",", "California"],
      "labels": ["B-ORG", "I-ORG", "O", "O", "O", "B-LOC", "O", "B-LOC"]
    }
  ],
  "metadata": null
}

Saving Output to File

Save results to a file:

encoderfile infer "Sample text" -o results.json

Process multiple inputs and save:

encoderfile infer "First input" "Second input" "Third input" --out-dir output.json

Configuration

The CLI relies on external configuration to determine:

• Model type (Embedding, SequenceClassification, TokenClassification)
• Model path and parameters
• Server settings

Ensure your configuration is properly set before running commands. Refer to the main encoderfile configuration documentation for details.

Error Handling

The CLI will return appropriate error messages for:

• Invalid configuration (e.g., both servers disabled)
• Missing required arguments
• Model loading failures
• Inference errors
• File I/O errors

Examples

Basic Inference Workflow

# Run inference
encoderfile infer "Hello world"

# Save to file
encoderfile infer "Hello world" -o embedding.json

Server Workflow

# Terminal 1: Start server
encoderfile serve --http-port 8080

# Terminal 2: Make HTTP requests (using curl)
curl -X POST http://localhost:8080/infer \
  -H "Content-Type: application/json" \
  -d '{"inputs": ["Hello world"], "normalize": true}'

Batch Processing

# Process multiple inputs at once
encoderfile infer \
  "First document to analyze" \
  "Second document to analyze" \
  "Third document to analyze" \
  --out-dir batch_results.json

Custom Server Configuration

# Run on specific network interface with custom ports
encoderfile serve \
  --http-hostname 192.168.1.100 \
  --http-port 3000 \
  --grpc-hostname 192.168.1.100 \
  --grpc-port 50052

Troubleshooting

Both servers cannot be disabled

Error: "Cannot disable both gRPC and HTTP"

Solution: Enable at least one server type:

encoderfile serve --disable-grpc  # Keep HTTP enabled
# OR
encoderfile serve --disable-http  # Keep gRPC enabled

Output not appearing

If output isn't visible, check:

1. Ensure you're not redirecting output to a file unintentionally
2. Check file permissions if using --out-dir
3. Verify the model is correctly configured

Model type detection

The CLI automatically detects model type from configuration. If inference behaves unexpectedly:

1. Verify your model configuration
2. Ensure the model type matches your use case
3. Check model compatibility

Complete Workflow Example

Here's a complete workflow from model export to deployment:

Step 1: Export Model to ONNX

# Export a HuggingFace model to ONNX format
optimum-cli export onnx \
  --model distilbert-base-uncased-finetuned-sst-2-english \
  --task text-classification \
  ./models/sentiment-model

Step 2: Create Configuration File

Create sentiment-config.yml:

encoderfile:
  name: sentiment-analyzer
  version: "1.0.0"
  path: ./models/sentiment-model
  model_type: sequence_classification
  output_path: ./build/sentiment-analyzer.encoderfile

Step 3: Build Encoderfile Binary

# Build self-contained binary
./target/release/cli build -f sentiment-config.yml

This creates: ./build/sentiment-analyzer.encoderfile

Step 4: Run Inference

Option A: Start server and use HTTP/gRPC

# Start server
./build/sentiment-analyzer.encoderfile serve

# In another terminal - use HTTP API
curl -X POST http://localhost:8080/predict \
  -H "Content-Type: application/json" \
  -d '{"inputs": ["This is amazing!", "This is terrible"]}'

Option B: Direct CLI inference

# Single inference
./build/sentiment-analyzer.encoderfile infer "This is amazing!"

# Batch inference
./build/sentiment-analyzer.encoderfile infer \
  "This is amazing!" \
  "This is terrible" \
  "This is okay" \
  -o results.json

Step 5: Deploy

# Copy binary to deployment location
cp ./build/sentiment-analyzer.encoderfile /usr/local/bin/sentiment-analyzer

# The binary is self-contained - no dependencies needed!
sentiment-analyzer serve --http-port 8080

Command Reference Summary

Command	Tool	Purpose
./target/release/encoderfile build -f config.yml	encoderfile	Build self-contained binary from ONNX model
./target/release/encoderfile version	encoderfile	Print version information
<model>.encoderfile serve	encoderfile	Start HTTP/gRPC inference server
<model>.encoderfile infer	encoderfile	Run single inference from command line
<model>.encoderfile mcp	encoderfile	Start MCP server

Additional Resources

• Getting Started Guide - Step-by-step tutorial
• API Reference - HTTP/gRPC/MCP API documentation
• BUILDING.md - Complete build guide with advanced configuration
• GitHub Repository - Source code and issues