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Training Lab fine-tunes a small open model on your own examples and gives it back to you: a LoRA adapter, a score report that shows how much it learned, and a downloadable GGUF when the conversion succeeds (gguf_available). You choose the data, the base model and a GPU block. We rent the GPU, run the job, and shut it down when it finishes. Singularity xMESH 0.8B was trained and evaluated in Training Lab.

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Three ways in

Dashboard

Pick a dataset and a model in Singularity Cloud, review the price, start.

API key

Upload datasets and start runs from code with an X-API-Key.

x402 (agents)

No account: get a 402 with the exact price, pay in stablecoin, poll.

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How a run works

  1. Data. Upload a JSONL file of chat examples, or describe the behaviour you want and have Training Lab generate a dataset. A dataset must be approved (schema-checked) before it can be trained on.
  2. Model. Choose a base model from the catalog (today Qwen 2.5 0.5B Instruct and Qwen 2.5 1.5B Instruct).
  3. GPU block. Choose a GPU and a number of hours. The price is fixed before you pay: hours x the GPU’s hourly training price.
  4. Training. The run reports progress while it trains. Every run keeps an evaluation split so the score report is measured, not guessed.
  5. Results. Download the adapter (and the GGUF when available), or deploy the model as a private AI Machine.

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Pricing and refunds

You pay for a prepaid block: the GPU hours you chose, at the hourly price in the catalog. Finishing early or cancelling does not refund the rest of the block. If something on our side stops a run before it trains (the GPU cannot start, the provider fails, the run never reports in), the charge is refunded to your credits automatically. Payments made over x402 are refunded the same way: as credits on the paying wallet’s account, never on-chain.

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Limits