Introducing Ponder Training.

Build your own models.

An experimental lab for post-training, continual learning and dedicated inference. Train with SFT, RL and model adapters, compare the results, and serve compatible saved versions through an API.

iOS app

Ponder Training

Train with a workflow and adapter, then evaluate the candidate in an experiment.

SFT
Learn from reviewed examples.
RL
Learn from rewards.
Decision model
Learn to choose between options.
Continual learning
Connect new tasks, choose review rules and a schedule, and compare each candidate within your spending limits.
Dedicated inference
Choose a compatible saved version, review deployment costs, and activate an API endpoint for your application.

Example: RL + LoRA

Training loopLoRA adapter
  1. RolloutsGenerate responses
  2. GradeScore with your rewards
  3. Update LoRATrain adapter weights
  4. CheckpointVersion your adapter
  5. EvaluateCompare with baseline
  6. ServeCall your model API
New outcomes. Next adapter.

Models

  • Qwen3 0.6B
  • Qwen3 8B
  • Qwen3.5 4B
  • Qwen3.5 9B
  • Qwen3.8 27B
  • Nemotron Lightning 3.5 30B A3B

OpenPond Desktop & Harness

Work with agents beside your local projects, then turn a dataset into an evaluation plan without leaving the app.

Explore Desktop

Start with a real task.

Get to work with OpenPond.