TT Local

TT Local runs Tuned Tensor-style fine-tuning jobs on your own machine. It keeps specs, datasets, model artifacts, events, reports, and dashboard state on local disk.

Usage docs:

https://tunedtensor.com/docs/local-training

Install

npm install -g @tuned-tensor/local
tt-local info

The bundled SFT, DPO, and Transformers evaluator path also needs uv:

uv --version

Custom training or evaluation workflows can use command entrypoints instead of uv.

The default uv project is included in the npm package at training/local-runner; using bundled training does not require cloning this repository.

Before the first real run, you can download the configured Hugging Face base model into paths.modelCache explicitly:

tt-local models prefetch tunedtensor.json --config local-runner.json

Without this step, the first non-dry tt-local run downloads the base model when baseline evaluation or training first loads it.

DPO

TT Local supports first-class offline DPO for text causal-LM models. Set training_method to dpo and provide a prebuilt preference JSONL training dataset:

{
  "training_method": "dpo",
  "dataset_prebuilt": {
    "training": "file://examples/dpo-preferences.jsonl",
    "format": "preference_jsonl"
  }
}

Each preference JSONL row must use explicit prompt, chosen, and rejected string fields:

{"prompt":"Summarize status: build passed.","chosen":"Build passed.","rejected":"The build failed."}

DPO v1 is text-only for the bundled uv trainer. Validation and reporting still use the existing baseline-vs-candidate evaluation loop, so provide dataset_prebuilt.test, dataset_prebuilt.validation, or normal spec_snapshot.examples with reference outputs for evaluation.

Command-backed workflows may use external model ids by setting spec_snapshot.base_model to an external:<id> or command:<id> value, for example external:karpathy/nanochat. The bundled uv trainer still requires a supported Hugging Face base model, but command trainers receive adapter-focused hyperparameters without injected LoRA/model-loader defaults. Custom hyperparameter keys are passed through to TT_HYPERPARAMETERS_PATH.

Continuing From a Fine-Tuned Model

Start another loop from an existing TT Local model by passing the stored local model id:

tt-local run tunedtensor.json --parent-model local-<previous-run-id>

You can also set the parent adapter artifact explicitly:

tt-local run tunedtensor.json --parent-model-artifact file:///path/to/model.tar.gz

For config-only workflows, put the same value in hyperparameters.parent_model_artifact. The parent adapter becomes the baseline for the new run, and the bundled SFT/DPO trainers load it before continuing LoRA training.

Non-Hugging Face artifacts can describe their layout in training.artifact:

{
  "training": {
    "backend": "command",
    "command": ["python", "train_adapter.py"],
    "artifact": {
      "framework": "nanochat",
      "format": "custom-directory",
      "entrypoint": "batch_command",
      "servable": false
    }
  }
}

Local Development

npm install
npm run typecheck
npm test
npm run build

Release notes are in CHANGELOG.md.

License

Apache-2.0