Flash

LoRA post-training for open-weight models: SFT, GRPO, and on-policy distillation. You describe a run in a TOML file, Flash allocates a GPU, trains, streams checkpoints, and serves the resulting adapter.

pip install freesolo-flash
export FREESOLO_API_KEY=fslo_...
flash login
flash train run.toml

The allocator picks the cheapest validated GPU class that fits the run — one dedicated worker allocation per run, on a single GPU today — supervised server-side (stall watchdog, bounded auto-retry resuming from the last streamed checkpoint, endpoint GC).

What this repository is

Flash is the client and control plane for Freesolo's hosted post-training service. This repository contains:

• the flash CLI (flash/cli/) — no declared runtime dependencies (commands that run an environment locally, such as flash env test, need the freesolo SDK),
• the FastAPI control plane (flash/server/) — run submission, auth, project scoping,
• the GPU worker and training recipes (flash/engine/) — verl plus colocated vLLM rollouts,
• the GPU provider substrate (flash/providers/) — pricing, allocation, submit/poll,
• the environment loading machinery (flash/envs/).

It does not contain everything needed to stand up an equivalent service from scratch. The following are Freesolo-operated and not part of this repository:

If you are evaluating Flash, the honest summary is: use it against the hosted service, or read and modify the training/provider code, which is self-contained and the most reusable part of the repository. Running your own end-to-end copy of the service is possible but requires replacing the components above — see Self-hosting.

Using the hosted service

Install the client and authenticate with a freesolo API key. flash login is not interactive — pass the key explicitly or export FREESOLO_API_KEY first:

pip install freesolo-flash
export FREESOLO_API_KEY=fslo_...
flash login          # validates the key and stores it in ~/.flash/config.json
flash whoami         # confirm the identity behind it

Every run names an environment, which supplies the task data and the reward or SFT target. Environments are published under a project, which scopes them to an organization:

flash projects create my-project                       # returns a project uuid
flash projects list                                    # look up existing uuids
flash env setup                                        # scaffold environment.py + dataset/train.jsonl
flash env push --project PROJECT_UUID --name my-env .  # returns an environment id

Project ids also appear in your Freesolo dashboard. Every training TOML carries a required top-level project = "<uuid>", which Flash validates against the authenticated organization before it allocates a run. Then describe the run and submit it:

project = "your-project-uuid"
model = "Qwen/Qwen3.5-4B"
algorithm = "sft"

[environment]
id = "your-name/my-env"

[train]
epochs = 1
max_examples = 1000
lora_rank = 32

flash train run.toml                  # submit, prints a run id
flash runs status RUN_ID               # follow it
flash models deploy RUN_ID             # serve the trained adapter
flash models chat RUN_ID -m "hello"    # talk to it

Run management lives under flash runs (status, log, cancel, checkpoint) and serving under flash models (deploy, chat, deployments, undeploy, export). flash models on its own lists supported base models and flash gpus lists GPU classes with estimated $/hr. To copy a finished adapter into your own HuggingFace repo:

flash models export --adapter-id RUN_ID --repository your-org/your-repo

Intermediate RL checkpoints are deployable too — list them with flash runs checkpoint RUN_ID, then pass RUN_ID/step-N as the adapter id.

There are no built-in task environments — the environment you push defines the task. Single-turn and bounded multi-turn environments are supported.

Calling a deployed adapter from your own app

Deploy once, then POST chat requests with your API key:

export FLASH_API_URL=https://flash.freesolo.co
export FREESOLO_API_KEY=fslo_...
export RUN_ID=flash-1782194170-ce1cfcff

curl -X POST "$FLASH_API_URL/v1/runs/$RUN_ID/deploy" \
  -H "Authorization: Bearer $FREESOLO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"dry_run": false}'

curl -X POST "$FLASH_API_URL/v1/runs/$RUN_ID/chat" \
  -H "Authorization: Bearer $FREESOLO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "messages": [{"role": "user", "content": "Write a two-sentence summary of the run."}],
    "temperature": 0.0,
    "max_tokens": 256
  }'

The response uses the OpenAI chat-completions shape; read choices[0].message.content. The run id is the adapter id for serving. If the run is not deployed yet, /v1/runs/<run_id>/chat returns 409 with a hint to deploy first.

Prefer the control-plane endpoint over calling the serving backend directly: it enforces run ownership and forwards per-run serving options such as thinking-mode parity.

Working on the code

The test suite is CPU-only and offline by default. No GPU, no network, no credentials:

uv sync --extra server --dev
uv run pytest -q                         # ~170 test files, offline
uv run ruff check .                      # lint

Those three are exactly what CI runs (.github/workflows/ci.yml).

To exercise the CLI from a dev checkout, invoke the module rather than the flash script:

uv run python -m flash.cli --help

The --dev group installs runpod-flash, which also declares a flash console script, so uv run flash in this environment may launch RunPod's CLI instead of this one. python -m flash.cli is unambiguous. Installed users are unaffected.

Formatting is not enforced repo-wide yet, so run ruff format on the files you touched rather than the whole tree. See CONTRIBUTING.md for the branching model — in short, pull requests go into dev.

Layout

• flash/catalog.py — curated model catalog (Qwen3.5 and Qwen3.6, dense and MoE), VRAM-fit sizing, and each model's thinking capability
• flash/schema/, flash/spec.py — TOML to JobSpec
• flash/runner/ — server-side run supervisor (durable job handle, retries, cost guard, endpoint GC)
• flash/providers/ — GPU substrate (pricing, GPU classes, durable submit/poll, preflight) behind the base.Provider protocol, with allocator.py picking the cheapest fitting class
• flash/engine/ — the on-GPU worker (verl + colocated vLLM rollouts; distillation scores on-policy student samples against a remote teacher) and the shared recipe. SFT targets and RL rewards route through the active environment, so task-specific grading lives with the example, not in the engine
• flash/envs/ — environment registry and the adapter that loads Freesolo SDK environments onto the worker's interface
• flash/serve/, flash/server/ — serving client and the FastAPI control plane (run via the separate flash-server command)
• tests/ — pytest suite (CPU-only, offline-by-default)

Self-hosting

You can run your own control plane, but read this first — it is an operator deployment, not a one-command install.

The control plane needs the server extra — the base install above is client-only and flash-server will refuse to start without it:

pip install 'freesolo-flash[server]'

flash-server then fails fast at startup unless all of the following are present (see flash/providers/preflight.py and .env.example). Note it reads the PROCESS environment, so load your .env (set -a && . ./.env && set +a) rather than relying on the file being present:

• RUNPOD_API_KEY — two or more distinct RunPod account keys, comma-separated. A single-account pool cannot reap or fail over across accounts, so the preflight rejects it.
• LAMBDA_API_KEY — Lambda Cloud API key.
• HF_TOKEN — write access to the dataset repos flash creates for artifacts. The repo is assigned by the control plane and scoped to the environment, so runs sharing an environment share a repo, each under its own prefix.
• FREESOLO_INTERNAL_KEY — control-plane authentication. Requests presenting this key authenticate as a single service identity with no network call, which is the path to use if you are not integrating with Freesolo identity.
• GITHUB_TOKEN — access to the managed environment repository.

Beyond credentials, three seams point at Freesolo services and would need replacing for a fully independent deployment:

1. User authentication. Unknown bearer tokens are verified against {FREESOLO_BASE_URL}/api/auth/verify (flash/server/auth.py). Only the internal-key path works without the Freesolo backend.
2. Environments. Publishing and managed-slug loading target a private environment repository (flash/server/envs.py, flash/envs/loader.py).
3. Serving. flash/serve/ is a client for the Freesolo multi-LoRA serving app; point it elsewhere with FREESOLO_SERVING_URL, but this repository does not include a serving backend.

The GPU worker image is public and can be pulled directly. It is published under an explicit CUDA tag, not latest:

docker pull ghcr.io/freesolo-co/flash-worker:cu128

Release channels

Two channels are published to PyPI from the same source, distinguished by one line in flash/_channel.py (CHANNEL):

Each environment holds exactly one channel: both packages ship the same import package (flash/) with one baked CHANNEL line, so installing both into the same environment makes the later install win for both CLIs. For side-by-side prod and staging, install each channel in its own virtualenv (or via pipx, which isolates per tool). The dev build is produced by scripts/build_dev_dist.py, which renames the package/CLI and flips CHANNEL to dev before uv build.

Within any single commit the two version fields are locked together: [project].version and [tool.flash-dev].version must match (CI enforces this via .github/workflows/version-parity.yml), so cutting a release means bumping both together. The published channels can still differ, because dev publishes on merge to dev while prod only publishes once dev is promoted to main — so freesolo-flash-dev is normally one or more versions ahead of freesolo-flash.

Either CLI still honours an explicit FLASH_API_URL / the login --api-url flag; the channel only sets the default.

Contributing

See CONTRIBUTING.md. Security issues: SECURITY.md — do not open a public issue.

License

Apache-2.0. See LICENSE and NOTICE.