Training

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

TrainingConfig

Configure base model, training steps, and optional LoRA parameters:

Field	Type	Default	Description
base_model	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:]

Methods

estimate_cost

Estimate training cost before running:

from lightningrod.training import TrainingConfig

config = TrainingConfig(
    base_model="Qwen/Qwen3-4B-Instruct-2507",
    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}")

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}")

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_fine_tuning.ipynb for the full workflow.