Core Capabilities
Workspaces
Organize your team, datasets, training runs, models, and workspace repository access with role-based permissions.
Training Runs
Submit, monitor, and manage RL training runs with configurable hyperparameters and checkpoint cadence.
Datasets
Upload and validate JSONL, CSV, or Parquet datasets up to 5 GB for training.
Models
List supported base models and deploy trained LoRA models for inference.
Monitoring
Track training run status, metrics, checkpoints, and outputs.
Git Integration
Create or connect a workspace repository to sync rollouts and configs automatically.
How It Works
The typical workflow from setup to deployed LoRA model follows five stages:Complete onboarding
Start with Onboarding to create or join a platform workspace, connect GitHub, clone the workspace repository, install the CLI, and verify local workspace context.
Choose a first workflow
Run the included Multiply example for a known-good first training run, or use Create Your Own Rollout when you already have a task or dataset.
Push and submit an evaluation run
Push rollout changes to GitHub. Git Sync publishes the code version, then
osmosis eval submit starts an evaluation run against a platform dataset to catch dataset, dependency, workflow, and grader issues before a training run.Submit a training run
Once evaluation run results look healthy,
osmosis train submit starts the training run.Ready to Get Started?
Onboarding
Follow the full workspace setup flow for creators and invited members.
Quickstart
After onboarding, run the included example from evaluation run to training run.