Training inputs
Datum
A single training example: a
ModelInput paired with a dict of TensorData loss function inputs.ModelInput
A tokenized prompt, represented as a list of
ModelInputChunk objects. Construct with ModelInput.from_ints(token_ids) for the common case.ModelInputChunk
A discriminated union of
EncodedTextChunk (a list of token IDs) and ImageChunk (a base64-encoded image with an expected token count).TensorData
A serializable tensor with a flat data list, a dtype string, and a shape. Convert to and from
torch.Tensor with TensorData.to_torch() and TensorData.from_torch(tensor).Configuration
SamplingParams
Controls for text generation:
temperature, top_p, top_k, max_tokens, seed, and stop.AdamParams
Optimizer hyperparameters:
learning_rate, beta1, beta2, eps, weight_decay, and grad_clip_norm.WandbConfig
Optional Weights & Biases settings (
project and an optional run name) passed to create_lora_training_client to stream training metrics.Results and handles
SampleResult
The full response from
sample(): a list of SampledSequence objects in sequences, the policy_version the sampler replica was running, and prompt_logprobs / topk_prompt_logprobs populated when the matching sample() flags are set.SampledSequence
A single generated sequence: a list of output token IDs, optional per-token log-probabilities, and a stop reason.
Checkpoint
Metadata for a saved checkpoint, populated by
list_checkpoints().CheckpointFilesResponse
A paginated list of presigned file URLs for a checkpoint, populated by
get_checkpoint_archive_url().CheckpointFile
One entry in a
CheckpointFilesResponse.presigned_urls list: a presigned URL plus relative_file_name, node_rank, size_bytes, and last_modified metadata.ServerCapabilities, SupportedModel
Returned by
ServiceClient.get_server_capabilities(); describe which base models the control plane can provision and on which GPU classes.OperationFuture[T]
A handle to a long-running training operation. Call
.result() or .result(timeout=seconds) to block until the operation completes and return the result. The forward and forward_backward methods return a ForwardBackwardFuture subclass, and save_weights_and_get_sampling_client returns a composed future; both expose the same .result() contract.ForwardBackwardOutput, OptimStepResponse, SaveWeightsResponse, SaveWeightsForSamplerResponse, LoadWeightsResponse, InitTrainerServerResponse, SampleResponse
Response payloads returned by the matching
TrainingClient and SamplingClient methods.