Tuned Tensor Documentation

End-to-end platform for shaping the behaviour of open-weight language models through instruction tuning. Define what your model should do, and the platform handles compilation, training, evaluation, and versioning.

The fastest way to get started is the open-source tt CLI. See the Quickstart.

Key Concepts

Behaviour Specs

A behaviour spec is a structured description of what you want your model to do. It includes:

System prompt — the persona and role of the model
Guidelines — rules the model should follow
Constraints — things the model must not do
Examples — input/output pairs demonstrating desired behaviour
Base model — which open-weight model to fine-tune

Runs

A run is a single end-to-end cycle: compile your spec into training data → augment with AI → fine-tune the model → auto-evaluate against your spec. Each run captures a snapshot of the spec at run time, so you can track how changes affect model behaviour over time.

Auto-Evaluation

After training completes, Tuned Tensor automatically evaluates the fine-tuned model by running each example from your spec and scoring the output. Scoring uses an LLM-as-judge for behavioural correctness, with fallback to similarity scoring. The platform also detects regressions — examples that scored worse than the previous run.

Playground

The Playground model list remains available for browsing your models, but interactive completions are disabled while hosted inference is not enabled.

Workflow

Define — Create a behaviour spec describing what your model should do
Run — Start a run. The platform compiles your examples, augments them with AI, fine-tunes the model, and evaluates it.
Inspect — Review per-example pass/fail results and check for regressions
Inspect — Use the Playground to browse base and fine-tuned model inventory
Iterate — Refine your spec and run again

Supported Models

Fine-tuning currently supports a restricted set of production base models. Prices below are per 1M training tokens, per epoch. See the Billing & credits page for details on how runs are charged.

Model	Size	Price / 1M tokens · epoch
`Qwen/Qwen3.5-2B`	2B	$0.45
`google/gemma-4-E2B-it`	E2B	$0.45
`ibm-granite/granite-3.3-2b-instruct`	2B	$0.45
`meta-llama/Llama-3.2-3B-Instruct`	3B	$0.55
`bigcode/starcoder2-3b`	3B	$0.55
`Qwen/Qwen3.5-4B`	4B	$0.70
`google/gemma-4-E4B-it`	E4B	$0.70
`microsoft/Phi-4-mini-instruct`	3.8B	$0.70

CLI & API

Every feature is accessible through the open-source tt CLI or the REST API. The dashboard is a convenience layer on top.

CLI (recommended) — Install with npm install -g @tuned-tensor/cli. Open source (MIT) on GitHub. See the CLI Tool docs for installation and usage.
REST API — Base URL: https://tunedtensor.com/api/v1. All endpoints accept Authorization: Bearer tt_... API key authentication or session cookies from the dashboard.