<h1 align="center">Tunedmodel</h1>
<p align="center">Opinionated, end-to-end framework for shipping fine-tuned language models.</p>
<p align="center"><a href="https://github.com/tunedmodel/tunedmodel">github.com/tunedmodel/tunedmodel</a></p>
<p align="center">
  <a href="https://github.com/tunedmodel/tunedmodel"><img alt="GitHub stars" src="https://img.shields.io/github/stars/tunedmodel/tunedmodel?style=social"></a>
  <a href="https://hex.pm/packages/tunedmodel"><img alt="Hex version" src="https://img.shields.io/hexpm/v/tunedmodel.svg?style=flat-square"></a>
  <a href="https://hexdocs.pm/tunedmodel/0.1.0/"><img alt="HexDocs" src="https://img.shields.io/badge/hexdocs-docs-8A2BE2?style=flat-square"></a>
  <a href="LICENSE"><img alt="MIT License" src="https://img.shields.io/badge/license-MIT-green?style=flat-square"></a>
</p>

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

## What is a Tunedmodel Model?
A Tunedmodel Model 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 `tunedmodel test` to run evals and gate releases.
- Customizable, templatizable multimodal chat (text + voice).
- Skill packs for common capabilities.
- Built-in coding assistant via `tunedmodel 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 `tunedmodel new` to scaffold a project through a wizard that sets AI parameters (name, languages, knowledge bases, tools, and chat/voice format).
- Use `tunedmodel code` as the default way to build the AI with a coding agent.
- Add data through connectors.
- Enable skill packs.
- Run `tunedmodel build` to build a local release bundle.
- Run `tunedmodel 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
If/when Tunedmodel is published to Hex, the package can be installed by adding `tunedmodel` to your list of dependencies in `mix.exs`:

```elixir
def deps do
  [
    {:tunedmodel, "~> 0.1.0"}
  ]
end
```

Documentation can be generated with [ExDoc](https://github.com/elixir-lang/ex_doc) and published on [HexDocs](https://hexdocs.pm).

HexDocs (v0.1.0): https://hexdocs.pm/tunedmodel/0.1.0/