Your local agent. Train and run models.
Use tt to work locally or in the cloud, inspect runs, and track usage. The agent runs on your machine.
Your TT access token includes model access for the agent, with no separate model key to set up. Or bring your own provider key and choose your model.
Direct local commands work without an account.
$ curl -fsSL https://tunedtensor.com/install.sh | shThe installer places tt on your PATH. Local adapter training and serving need uv and a compatible NVIDIA GPU on Linux.
tt
agent tunedtensor/managed · workflow model base
ctrl+c stop/clear · ctrl+d exit · /help commands · tab complete
Ask TT anything. Known commands run directly.
› How do I inspect my latest local run?
Use runs list to find it, then runs report <run-id> to read the results.
› runs list▊Local agent
Inspect runs and plan your next step.
Ask TT to check your setup, explain a run, or help prepare a spec. Review proposed spec changes before applying them with /approve.
Use the same CLI for local files, cloud resources, diagnostics, and usage. The dashboard shows cloud progress and results.
› init --model Qwen/Qwen3.5-2B
✓ Created tunedtensor.json. Add your instructions and examples.
› doctor
✓ GPU, uv, and spec look ready
› pipeline run --spec tunedtensor.json
pass rate 61.5% → 89.0% (+27.5 pts)Train and evaluate
Compare the base model with your adapter.
Define your task, examples, constraints, and base model in tunedtensor.json. TT evaluates the base and trained models on the same cases with the same scorer.
Local adapter runs set aside examples for evaluation. Prebuilt datasets require a separate validation or test split. Inspect the scores and individual regressions in the report.
› runs report local-8f3a
base and tuned scores · case results · evaluation gates
› models verify local-8f3a
✓ adapter checksum matches the run record
› models serve local-8f3a
OpenAI-compatible http://127.0.0.1:8000/v1Serve your model
Verify the files and start a local server.
Local runs keep specs, adapters, events, and reports on disk. Verify the model files before serving them. To make a model active, configure a generalRegression evaluation and check that its gate passes.
Adapter serving loads the instructions saved with the run. Use --spec when serving a base model, or to supply a matching copy of an adapter's spec.
Your hardware
Supported local checkpoints.
These checkpoints support text training with LoRA adapters on CUDA. The 30B models target NVIDIA DGX Spark hardware. TT downloads weights from Hugging Face unless they are cached. Local evaluation scores the model outputs without calling a separate judge model.
Default text model
Qwen/Qwen3.5-2B
The usual starting point for classification, extraction, routing, and policy tasks.
DGX Spark
nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
A 30B model for Spark hardware. Training adapts shared attention and Mamba projections; routed experts stay frozen.
Muse text training
meta-models/Muse-Glimmer-30B
Trains the text component of this checkpoint. Local examples must contain text; vision weights stay frozen.
Best fit
Tasks with clear success criteria.
Tuned Tensor is for focused application tasks. Provide representative examples, score the outputs, and decide whether the fine-tune is worth keeping.
Structured extraction into strict JSON
Classification, safety, and triage
Routing and tool selection
Format, tone, and policy adherence
Example evaluation
Email triage: test pass rate rose 27.5 percentage points.
We fine-tuned Qwen 3.5 2B on 8,000 public training examples. On a test sample of 200 examples excluded from training, pass rate improved from 61.5% to 89.0% and average score improved from 0.537 to 0.862.
These results are specific to this task and evaluation setup. Test on data that reflects your production workload.
Base pass rate
61.5%
Tuned pass rate
89.0%
Pass rate change
+27.5 pts
Test cases
200
Before you run
Check your hardware and evaluation data.
Linux and NVIDIA CUDA
Local adapter training and model serving need Linux, a compatible NVIDIA GPU, and uv. Evaluation may use CPU. Run tt hardware to check your machine.
Model support
Use a supported checkpoint from the list above. GPU memory and training settings determine which models fit your machine.
Evaluation coverage
Reports measure performance on the cases you provide. Include examples of the inputs your application will receive.
Accounts and model access
Direct local commands need no account. Use a TT token for managed agent access and cloud operations, or bring your own provider key for the agent.
Get involved
Install TT. Connect your agent.
The CLI is Apache-2.0. Sign in to create an access token, then run tt auth login to connect your local agent and cloud account. Your own provider key remains an option.