Get started in minutes
Install the CLI and go from an empty directory to a deployed model in a few
minutes.
What you can do
Fine-tune a model on your own task
Write a TOML config, run one command, and Flash trains a LoRA adapter (a small set of add-on weights) on top of a supported base model. Pick the model and task; Flash handles the training infrastructure. See Training.Pick how the model learns
One line of config selects the algorithm. Use SFT when you already have example answers, GRPO when you can score an output but can’t hand-write the perfect one, and OPD when a stronger model already does the task and you want a small one to match it. See Training.Serve it behind an OpenAI-compatible API
flash models deploy registers the adapter with managed serving, then
flash models chat or any OpenAI client can call it with your Freesolo key. See
Deploy & chat.
Or serve it in your own cloud account
flash serve deploy provisions the same serving stack in your own Modal or
RunPod account, so the GPU runs and bills there and you call the provider’s
endpoint directly. See
Serve in your own account.
See the cost before you spend
--cost returns a pre-flight estimate without starting paid training or
allocating a training GPU. Successful runs bill at the accepted quote,
cancellations are prorated and capped at that quote, and serving is billed per
token. See Cost and billing.
Next steps
Quickstart
Install the CLI, log in, and ship your first run in a few minutes.
How Flash works
The loop behind a run: base models, environments, algorithms, serving.
Training
Write a config, submit a run, and follow it to completion.
Deploy & chat
Serve an adapter, then chat with it over an OpenAI-compatible API.