Cybertron is a pure Go package that provides a simple and easy-to-use interface for cutting-edge Natural Language Processing (NLP) technologies.

In essence, it enables Go developers to use state-of-the-art neural technologies i.e. Transformers, without having to learn other languages or worry about heavy deep learning frameworks (thus, the deployment is just a single executable for your server!).

Luckily, pre-trained /fine-tuned Transformer models exist for several languages and are publicly hosted on the HuggingFace models repository.

A unique feature of Cybertron is its compatibility with HuggingFace Transformers: it can run inference on PyTorch pre-trained models after they have been automatically downloaded and converted to the Spago format.

Cybertron currently supports a few architectures (BERT, BART, and derivatives), and we’re seeking collaborators to speed up its development!

Supported tasks

  • Zero-Shot Text Classification
  • Machine Translation



Clone this repo or get the library:

go get -u

Cybertron supports two main use cases, which are explained more in detail in the following.

Server mode

Settings are configured in a .env file, which is automatically loaded by Cybertron. Alternatively, it also accepts configurations via flags.

For a complete list run:

GOARCH=amd64 go run ./cmd/server -h


Usage of server:
  -address value
        server listening address
  -allowed-origins value
        allowed origins (comma separated)
  -loglevel value
        zerolog global level
  -model value
        model name (and sub-path of models-dir)
  -model-conversion value
        model conversion policy ("always"|"missing"|"never")
  -model-conversion-precision value
        floating-point bits of precision to use if the model is converted ("32"|"64")
  -model-download value
        model downloading policy ("always"|"missing"|"never")
  -models-dir value
        models's base directory
  -network value
        network type for server listening
  -task value
        type of inference/computation that the model can fulfill ("text2text"|"zeroshotclassification")
  -tls value
        whether to enable TLS ("true"|"false")
  -tls-cert value
        TLS cert filename
  -tls-key value
        TLS key filename

To run Cybertron in server mode for Machine Translation (e.g. en to it) with default settings, simply create a .env file in the current directory:

echo "CYBERTRON_MODEL=Helsinki-NLP/opus-mt-en-it" > .env
echo "CYBERTRON_MODELS_DIR=models" >> .env
echo "CYBERTRON_MODEL_TASK=text2text" >> .env

and execute the following command:

GOARCH=amd64 go run ./cmd/server

To test the server, run:

curl -X 'POST' \
  '' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "input": "You must be the change you wish to see in the world.",
  "parameters": {}

Library mode

Several examples can be leveraged to tour the current NLP capabilities in Cybertron. A list of the demos now follows.

Machine Translation

GOARCH=amd64 CYBERTRON_MODEL=Helsinki-NLP/opus-mt-en-it CYBERTRON_MODELS_DIR=models go run ./examples/textgeneration

Zero-Shot Text Classification

GOARCH=amd64 CYBERTRON_MODEL=valhalla/distilbart-mnli-12-1 CYBERTRON_MODELS_DIR=models go run ./examples/zeroshotclassification


Cybertron’s pricipal dependencies are:

  • Spago – a lightweight self-contained machine learning framework in pure Go
  • GoPickle – a Go module for loading Python’s data serialized with pickle and PyTorch module files
  • GoTokenizers – Go implementation of today’s most used tokenizers

The rest are mainly for gRPC and HTTP API developments.

Dev Tools

To get started, install the following tools:

go install \   \                                                                                                                                       \ \

Then run the following command to generate the gRPC and HTTP APIs:

go generate ./...


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