Training

Create and manage LoRA fine-tuning jobs on Lightning Rod datasets. Access via lr.training on your LightningRod client.

Training jobs use one of two configuration types: GRPO (reinforcement-style training for forecasting) or SFT (supervised fine-tuning on labeled question–answer pairs). Pass the matching SDK config class; the API stores a discriminated config on the job. When you read job.config from get or list, it is a generated GRPOTrainingConfig or SFTTrainingConfig from the API (not the thin SDK wrapper classes).

Before starting a training job, prepare your generated dataset with prepare_for_training. This filters invalid samples, deduplicates, and creates the training-ready train_dataset you pass to lr.training.estimate_cost(...), lr.training.create(...), or lr.training.run(...).

GRPOTrainingConfig

Use for forward-looking / GRPO training. Configure base model, training steps, and optional LoRA parameters:

Field	Type	Default	Description
base_model_id	str	—	HuggingFace model ID for LoRA base (e.g. "Qwen/Qwen3-8B")
training_steps	int	—	Number of training loop iterations
batch_size	int | None	None	Rows per batch; used to slice train_rows each step
lora_rank	int | None	None	LoRA adapter rank
learning_rate	float | None	None	Step size for weight updates; higher values learn faster but may overshoot
adam_beta1	float | None	None	Exponential decay rate for first-moment estimates (moving average of gradients)
adam_beta2	float | None	None	Exponential decay rate for second-moment estimates (moving average of squared gradients)
num_rollouts	int | None	None	Samples per prompt for GRPO
max_response_length	int | None	None	Max tokens for sampling
start_idx	int | None	None	Row index to skip at start; train_rows = train_rows[start_idx:]
save_frequency	int | None	None	Checkpoint frequency in training steps (server default if omitted)

SFTTrainingConfig

Use for supervised fine-tuning. Same core hyperparameters as GRPO where applicable, plus SFT-specific fields. No num_rollouts or max_response_length.

Field	Type	Default	Description
base_model_id	str	—	HuggingFace model ID for LoRA base
training_steps	int	—	Number of training loop iterations
batch_size	int | None	None	Rows per batch
lora_rank	int | None	None	LoRA adapter rank
learning_rate	float | None	None	Step size for weight updates
adam_beta1	float | None	None	Adam β₁
adam_beta2	float | None	None	Adam β₂
start_idx	int | None	None	Row index to skip at start
save_frequency	int | None	None	Checkpoint frequency in training steps (server default if omitted)
resume_from	str | None	None	Resume from a Tinker checkpoint path
epochs	int | None	None	Passes over the training data (server default if omitted)

Methods

estimate_cost

Estimate training cost before running:

from lightningrod.training import GRPOTrainingConfig

config = GRPOTrainingConfig(
    base_model_id="openai/gpt-oss-120b",
    training_steps=50,
)

cost_estimate = lr.training.estimate_cost(config, dataset=train_dataset)
print(f"Estimated cost: ${cost_estimate.total_cost_dollars:.2f}")
print(f"Effective steps: {cost_estimate.effective_steps}")
print(f"Train tokens: {cost_estimate.train_tokens}")

For SFT, use SFTTrainingConfig the same way.

Returns EstimateTrainingCostResponse with total_cost_dollars, prefill_tokens, sample_tokens, train_tokens, effective_steps, notes, and optional warning_message.

create

Create a training job without waiting:

job = lr.training.create(config, dataset=train_dataset, name="My fine-tune")
print(job.id, job.status)

run

Create a job and poll until completion. In notebooks, shows a live progress display. Outside notebooks, raises on failure:

job = lr.training.run(
    config,
    dataset=train_dataset,
    name="Forecasting fine-tune",
    poll_interval=15.0,
)
print(f"Model ID: {job.model_id}")

Training job fields: On a completed job, job.model_id is the final adapter. job.model_id_by_step maps training step (string keys) to intermediate checkpoint model IDs so you can run evals or inference on a specific checkpoint, not only the final one. See Evaluating Intermediate Checkpoints.

get

Fetch a single job by ID:

job = lr.training.get(job_id)

list

List training jobs with pagination and optional status filter:

response = lr.training.list(page=1, limit=10, status="completed")
for job in response.jobs:
    print(job.id, job.model_id)

Example

See notebooks/getting_started/05_grpo_training.ipynb for GRPO forecasting workflow and notebooks/getting_started/06_sft_training.ipynb for SFT.