Find bad data before the trainer does.
Surface broken schemas, duplicates, leakage, and missing provenance before compute begins.
In development · Local-first AI model studio
Build your own AI on your Mac. Keep the proof.
Prepare the data, establish a baseline, fine-tune an open model, and prove the candidate is better—all inside one portable project.
Tensor Cortex StudioInvoice extractionLocal project · Revision 03
Core journey · 6 of 7 / Experiment decision
Candidate held for review.
Evaluation suiteBaselineCandidateGate
Structured outputRecordedImprovedPass
Schema validityRecordedImprovedPass
Regression setClear2 failuresHold
The run completed. The package did not. Review the regression examples before promoting this candidate.
Open failed examples →A training run is an event. Evidence is the product.
Open-model tools can already launch a run. The difficult part is preserving the task, dataset, baseline, method, hardware, holdout, and outcome well enough to make a defensible decision.
Keep the method with the result.
Pin the model, configuration, environment, and checkpoints so a useful run can be recovered.
Let regressions veto a pretty chart.
Compare task quality and holdout behavior; do not confuse lower training loss with a better model.
02 / One continuous project
From raw examples to a result you can defend.
Studio is designed around one ordered experiment record, not a collection of disconnected utilities.
Read the product documentation- 01Task contract
Define
Name the task, the behavior you need, and the constraints that decide whether the project is useful.
- 02Dataset revision
Prepare
Inspect canonical JSONL, isolate quality issues, preserve provenance, and freeze honest evaluation splits.
- 03Baseline evidence
Baseline
Measure the unchanged base model before spending time on a customization run.
- 04Reproducible run
Train
Run a bounded MLX and LoRA experiment on Apple Silicon with the configuration recorded beside it.
- 05Decision record
Compare
Put the candidate against the baseline, holdout, regressions, and target-device constraints.
- 06Portable artifact
Package
Keep the adapter, experiment, evaluation, provenance evidence, and known limits together.
03 / The decision layer
A run can finish and still fail.
Tensor Cortex Studio is meant to make that distinction visible. Baselines, holdouts, regressions, latency, hardware, and limitations stay attached to the candidate that produced them.
“Completed” describes compute. “Improved” requires evidence.
Promotion checklistIllustrative decision record
QuestionEvidenceDecision
Is the data usable?Revision and health reportReady
Did the task score improve?Baseline comparisonImproved
Did anything important regress?Frozen holdoutReview
Can another machine reproduce it?Manifest and environmentRecorded
Final decisionHold packageOne unresolved gate is enough.
04 / Open project contract
The experiment leaves with you.
The canonical project lives on your Mac. Source revisions, configuration, evidence, and export intent remain inspectable instead of disappearing behind a hosted dashboard.
- Local authority
- The Mac holds the canonical project copy.
- Open artifacts
- Portable files, not a platform hostage.
- Secret-free manifests
- Credentials stay outside the project contract.
project_id: invoice-extraction
schema_version: 1
dataset:
format: jsonl
revision: sha256:41c…
holdout: frozen
base_model:
source: open-model
revision: immutable
experiment:
method: lora
backend: mlx
evidence:
baseline: required
regressions: required
provenance: includedProject portableCredentials externalSource preserved
05 / Compute without captivity
The project stays. Compute changes.
Begin with the machine already on your desk. The same project contract can later reach more compute without changing who owns it.
- 01Your MacFirst target
Apple Silicon · MLX · local project
- 02Multi-MacPlanned
Free local network compute direction
- 03Tensor Cortex CloudPlanned
Managed, budgeted acceleration
- 04BYOCFuture
Your provider account, one experiment model
Local training does not require a cloud account. Multi-Mac, Tensor Cortex Cloud, and BYOC are planned product directions—not generally available services today.
06 / Ownership boundary
Local-first is not a sync setting.
It decides which copy is authoritative.
Studio is designed so the local project remains canonical. Cloud compute, when explicitly selected, acts as a temporary execution resource rather than the permanent owner of your experiment.
- Explicit data-movement boundaries
- No silent cloud fallback
- Portable model and evidence package
Latest field note
Why You Need a Baseline Before Fine-Tuning a Local LLM
A practical protocol for measuring the base model, protecting the holdout, and deciding whether a local fine-tuning experiment actually improved the task.
Read the evidence noteQuestions, answered plainly
Before you mistake direction for availability.
01What is Tensor Cortex Studio?
Tensor Cortex Studio is a local-first environment for building task-specific open models on Apple Silicon. It is designed to help technical users prepare data, choose an appropriate customization method, establish a baseline, fine-tune or distill a model, evaluate the result, and export a portable package.
02Is Tensor Cortex Studio available to download?
Not yet. The product is under active development and there is no public application build today. The website documents the intended product boundary and will publish download, compatibility, and benchmark information only when supported by tested release evidence.
03Is it just a graphical wrapper around training commands?
No. The product is intended to connect method selection, dataset health, baseline evaluation, hardware-aware planning, training, regression checks, and packaging in one reproducible project. A lower training loss alone is not treated as proof that a model improved.
04Which computers will it support first?
The first product target is Apple Silicon Mac hardware and text decoder language models through an MLX-based workflow. A deliberately small supported model and task matrix will expand only after compatibility is measured.
05Does my data have to leave my Mac?
Local projects are designed to keep their canonical copy on the Mac, and local training does not require a cloud account. If a user later chooses managed cloud compute, the interface will show the region, estimated cost, data movement, and hard budget boundary before a job is submitted.
06Will local use be free?
The planned core Studio application, local training, and Multi-Mac compute are free. The commercial model is based on optional managed cloud compute and paid orchestration for users who connect their own cloud accounts. Final pricing will be published before those services become available.
07Can it train a foundation model from scratch?
That is not the initial product. Tensor Cortex Studio focuses on adapting existing open models with methods such as prompt design, retrieval, supervised fine-tuning, preference optimization, and distillation, then measuring whether the adapted model is better for a defined task.
08What will an experiment produce?
The intended output is more than a checkpoint: a portable model or adapter package accompanied by its dataset and model revisions, experiment configuration, evaluation comparison, hardware and cost measurements, provenance, and known limitations.
In developmentEvidence before promises
Watch Studio become real.
Tensor Cortex Studio is under active development. There is no public app download or generally available cloud service yet; published capabilities and benchmarks will be tied to reproducible product evidence.