tt - Tuned Tensor CLI

tt is the command-line tool for Tuned Tensor. Use it to define behaviour specs, validate them, launch fine-tuning runs, manage datasets, and download or serve trained models.

The main CLI documentation lives at tunedtensor.com/docs/cli. This README is a short install and development reference.

Install

npm install -g @tuned-tensor/cli
tt --version

Run from source:

git clone https://github.com/tunedtensor/tuned-tensor-cli.git
cd tuned-tensor-cli
npm install
npm run build
npm link

Quick Start

tt auth login
tt init --name "Customer Support Bot" --model Qwen/Qwen3.5-2B

# Edit tunedtensor.json, then:
tt eval
tt push
tt runs estimate <spec-id>
tt runs start <spec-id>
tt runs watch <run-id>

To continue training from a completed fine-tuned model artifact:

tt runs start <spec-id> --parent-model <model-id>

Useful discovery commands:

tt specs list
tt datasets list
tt runs list
tt models list
tt models base
tt balance

Label real, unlabeled data with a teacher model (JSONL with {"input": ...} rows, or CSV with --input-column; up to 50,000 rows / 50 MB). Labeling runs as a managed cloud workflow — upload and disconnect; the teacher drafts outputs under your spec's system prompt and you review before anything trains:

tt label upload tickets.csv --spec <spec-id> --input-column body --watch
tt label watch <job-id>                     # re-attach to progress any time
tt label rows <job-id> --status labeled     # review the teacher's drafts
tt label accept <job-id> --all              # or accept/reject/edit by row
tt label promote <job-id> --name tickets-v1 # becomes a validated dataset
tt runs start <spec-id> --dataset <dataset-id>

Export a model to GGUF and package it for Ollama (so it's pluggable like any other local model, e.g. in OpenClaw via Ollama's native /api/chat):

# Convert + quantize to GGUF, write a Modelfile, and run `ollama create`
tt models export <model-id> --format gguf --quant q4_k_m --ollama

# Inspect the planned llama.cpp / ollama commands without running them
tt models export <model-id> --quant q8_0 --ollama --print-command

This wraps llama.cpp's convert_hf_to_gguf.py + llama-quantize and Ollama's ollama create. Point tt at your llama.cpp checkout with --llama-cpp <dir> (or --convert-script / --quantize-bin); with --ollama the behaviour spec's system prompt is embedded as the Modelfile SYSTEM block.

For the full command reference, including dataset-backed runs, long-example policies, eval token budgets, preflight run estimates, continued fine-tuning, evaluation caps, local model serving, configuration, and billing, see the CLI docs.

Development

npm install
npm run build
npm run dev
npm run typecheck
npm test

License

MIT