OpenPond labNotes on models, training and experiments.

Build your own models.

An experimental lab for model training and weight modifications. Run SFT and RL, train LoRA adapters, and build continual-learning loops with our harness or your own.

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Model modifications

Train weights, edit knowledge, or work with activations. Explore how each method changes a model.

SFT & LoRA

Train on examples of the answers you want. SFT supplies the learning objective; LoRA updates a small adapter while the base weights stay frozen.

Reviewed examplesInputs and approved answers
Supervised trainingLearn from target-token loss
Trained adapterSave with its exact base model
Support conversation
Frozen base model
Train LoRA weights
Your answer format
Added adapter weights

ExampleTeach a support model to return your response format from reviewed conversations.

Ponder Training

Train from examples with supervised fine-tuning (SFT), or use reinforcement learning (RL) to learn from rewards. Ponder Training handles the training run, checkpoints and model serving from the same experiment setup.

SFT & LoRA
Use reviewed input-and-answer pairs to teach a response format or task. Train a LoRA adapter, then evaluate against your starting model.
RL with GRPO
Generate groups of responses, score them with your graders, and update the policy from those rewards. Keep evaluation tasks separate from training data.
Continual learning
Collect new examples and continue from an existing trained version. Review each update before choosing which version to serve.
Explore Ponder Training
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.

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.