Enchant

Opinionated, end-to-end framework for shipping fine-tuned language models.

What is Enchant

Enchant is an opinionated, full-stack framework that helps developers build fine-tuned language models without having to wire up a training pipeline, inference infrastructure, or an app experience manually.

What is an Enchant Project?

An Enchant Project is a locally trained language model that ships with a completely customizable chat experience, ready to run locally or deploy.

It typically includes:

Training recipe: data sources plus repeatable training/fine-tuning steps.
Evaluation suite: datasets, tasks, scoring, and gates.
Model artifacts: weights/adapters, tokenizer, and model metadata.
Configuration: defaults, limits, policies, routing, and connectors.
Runtime: processes/services that load the model and expose it.
Chat channels: templatizable multimodal chat (text + voice).

Core mission

Make it dramatically easier to build, evaluate, and release language models as shippable products.

Reduce the friction from idea → shippable model.
Make training and evaluation repeatable build steps.
Make shipping a model feel as normal as shipping software.

Tech stack (Elixir)

The stack is centered on Elixir.

Potential ecosystem components we may use:

Phoenix + Plug: HTTP APIs and web runtime.
Phoenix LiveView: interactive pages and chat without heavy frontend JS.
Phoenix PubSub + Presence: realtime events, channels, and multi-client state.
Ecto: data access, migrations, validation, and multi-database support.
SQLite/Postgres: persistence options depending on deployment footprint.
Oban: durable background jobs and workflows.
Broadway: backpressure-aware data ingestion pipelines.
Nx/Axon + Explorer: numerical computing, model execution, and dataframes.
Bumblebee: HuggingFace model integration on top of Nx.
Livebook: reproducible notebooks for experiments, evals, and training recipes.
Telemetry + OpenTelemetry: metrics, traces, and performance instrumentation.
Finch/Tesla: HTTP clients for connectors and integrations.
Release tooling (mix releases): repeatable, self-contained deployment artifacts.

Key capabilities

A shared project shape so teams can develop and use their own models without setting up the pipeline from scratch.
Data connectors for files, repos, and URLs.
Training recipes that produce repeatable release bundles.
Built-in testing via enchant test to run evals and gate releases.
Customizable, templatizable multimodal chat (text + voice).
Skill packs for common capabilities.
Built-in coding assistant via enchant code.
Release bundles with profiles and checksums.

Proposed features

Agentic reasoning (planning + tool use loops).
Pipeline to fine-tune gpt-oss-20b end-to-end (data → training → evals → release bundle).

Typical workflow

Run enchant new to scaffold a project through a wizard that sets AI parameters (name, languages, knowledge bases, tools, and chat/voice format).
Use enchant code as the default way to build the AI with a coding agent.
Add data through connectors.
Enable skill packs.
Run enchant build to build a local release bundle.
Run enchant start to start the AI runtime.
Customize the chat experience and ship the release bundle.

Who it’s for

Builders shipping local-first models end-to-end.
Engineers who want to move fast and ship models quickly.
Researchers who want to run experiments faster.

Installation (Elixir)

Enchant is built with Elixir.

Install Elixir (1.19+) and Erlang/OTP (26+).
Install Hex (usually comes with Elixir) via mix local.hex.