Train your own AI

Train builds a small assistant that is good at one job. Training teaches your AI how to do its job. Connected knowledge gives it the current information it needs to do that job.

Use examples of good answers, such as chat transcripts or question and answer pairs, to train behavior: the questions it asks, its terminology, its steps, and its tone. Connect data that changes or must stay exact, such as inventory, prices, SKUs, catalogs, and policies, as knowledge. Editing connected knowledge never requires retraining.

Training runs on this computer or a paired one. It needs a Mac with Apple Silicon or a computer with an NVIDIA GPU. The first run downloads the trainer (about 450 MB on a Mac, about 4 GB with NVIDIA's libraries) and the base model's training weights. The base model is not changed, and it stays available on its own.

The plan shows each computer's training fit: the memory training needs, downloads, disk space, and how long it takes. Another computer can train for this one; choose it in the Review step, and the trained AI comes back to this computer. Training waits for chats, automations, and benchmarks to finish before it starts, and a chat during training is still answered, with a note about how long training has left.

A deployed AI can also be exported as one GGUF file from the Deploy step, for LM Studio, Ollama, llama.cpp, and other GGUF tools. The file is a little larger than the base model. Its instructions and connected knowledge stay in Toskar; copy the instructions shown with the file to use as the system prompt elsewhere. A copy of Toskar from the Mac App Store trains on a paired computer unless the app includes the trainer.

1. To see how it works first, choose Try an example. It sets up a sample tire shop assistant with example chats, an inventory, and a policy document, and explains each step. Nothing trains until you press Train.
2. Open Train and choose Build a new AI. Describe the job in a sentence or two.
3. Choose a base model. Toskar recommends one for the job and shows its license, the memory training needs, and how long it takes.
4. Add material. For each file or paste, Toskar recommends Training, Knowledge, or Both and explains why. Review the recommendation before you add it.
5. Review the examples. Fix or exclude flagged examples. At least 10 usable examples are required; about 50 teach a workflow reliably.
6. Review the plan, pick Quick, Balanced, or Highest quality, and start training. You can cancel at any time.
7. Compare base and specialized answers on the test prompts. Evaluation is required before deploying.
8. Deploy. The AI appears in the Chat model menu and in the API as sai:<name>. Retraining creates a new revision; the deployed one keeps answering until you deploy the new one.