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.

Local Serving

Fine-tuned artifacts can be downloaded and served locally with the tt CLI. Use tt models serve to run a completed model with the behaviour spec prompt applied.

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
Serve — Use tt models serve to run the completed model locally with the behaviour spec prompt applied
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
`google/gemma-4-E2B-it`	E2B	$0.45
`Qwen/Qwen3.5-2B`	2B	$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
`google/gemma-4-E4B-it`	E4B	$0.70
`Qwen/Qwen3.5-4B`	4B	$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.