encoderfile

What it does

encoderfile turns transformer encoders into self-contained executables for local inference. It supports embedding extraction, sequence classification and token classification using supported ONNX-exported models.

Developers can run a packaged model as a CLI, HTTP or gRPC service, or MCP server. This is useful for local retrieval and classification pipelines where a full Python environment is inconvenient. Building a package requires a compatible model and configuration; the model’s accuracy, license and resource requirements remain separate from the packaging tool.

What it can do

• Embedding inference
• Sequence classification
• Token classification
• Portable model packaging
• Local service deployment

Before you choose

• Only supported model architectures can be packaged.
• Model weights retain their own licensing and resource requirements.

Listed under

• Coding
• Sandboxes and infrastructure

Get started

1. Select a supported ONNX-exported encoder.
2. Create the package configuration and build the executable.
3. Test outputs and choose the required local interface.

Apache-2.0-licensed code is available without a software subscription. Hosting, hardware and paid model APIs are separate.

We couldn’t find published prices for encoderfile. See their pricing page ↗

Sources for this overview

Product pages, documentation, and pricing checked 30 Oct 2026. Each source supports part of the overview; availability and prices can change.

• encoderfile - Package transformer encoders into a single binary ↗www.mozilla.ai
• GitHub - mozilla-ai/encoderfile: Distribute and run transformer encoders with a single file. · GitHub ↗github.com

Company website

mozilla.ai: product, pricing and policy pages

Social accounts

Accounts the company links, and its posts on X

Human review

Checked by a person

knowledge_base

manual_review

Last checked 30 Oct 2026. Every field keeps its source and date; unknown means we couldn’t confirm it.