14 KiB
tinker.types.optim_step_request
AdamParams Objects
class AdamParams(StrictBase)
learning_rate
Learning rate for the optimizer
beta1
Coefficient used for computing running averages of gradient
beta2
Coefficient used for computing running averages of gradient square
eps
Term added to the denominator to improve numerical stability
tinker.types.optim_step_response
OptimStepResponse Objects
class OptimStepResponse(BaseModel)
metrics
Optimization step metrics as key-value pairs
tinker.types.model_input
ModelInput Objects
class ModelInput(StrictBase)
chunks
Sequence of input chunks (formerly TokenSequence)
from_ints
@classmethod
def from_ints(cls, tokens: List[int]) -> "ModelInput"
Create a ModelInput from a list of ints (tokens).
to_ints
def to_ints() -> List[int]
Convert the ModelInput to a list of ints (tokens) Throws exception if there are any non-token chunks
length
@property
def length() -> int
Return the total context length used by this ModelInput.
empty
@classmethod
def empty(cls) -> "ModelInput"
Create an empty ModelInput.
append
def append(chunk: ModelInputChunk) -> "ModelInput"
Add a new chunk, return a new ModelInput.
append_int
def append_int(token: int) -> "ModelInput"
Add a new token, return a new ModelInput.
tinker.types.weights_info_response
WeightsInfoResponse Objects
class WeightsInfoResponse(BaseModel)
Minimal information for loading public checkpoints.
tinker.types.checkpoint
Checkpoint Objects
class Checkpoint(BaseModel)
checkpoint_id
The checkpoint ID
checkpoint_type
The type of checkpoint (training or sampler)
time
The time when the checkpoint was created
tinker_path
The tinker path to the checkpoint
size_bytes
The size of the checkpoint in bytes
public
Whether the checkpoint is publicly accessible
ParsedCheckpointTinkerPath Objects
class ParsedCheckpointTinkerPath(BaseModel)
tinker_path
The tinker path to the checkpoint
training_run_id
The training run ID
checkpoint_type
The type of checkpoint (training or sampler)
checkpoint_id
The checkpoint ID
from_tinker_path
@classmethod
def from_tinker_path(cls, tinker_path: str) -> "ParsedCheckpointTinkerPath"
Parse a tinker path to an instance of ParsedCheckpointTinkerPath
tinker.types.checkpoint_archive_url_response
CheckpointArchiveUrlResponse Objects
class CheckpointArchiveUrlResponse(BaseModel)
url
Signed URL to download the checkpoint archive
expires
Unix timestamp when the signed URL expires, if available
tinker.types.sampled_sequence
SampledSequence Objects
class SampledSequence(BaseModel)
stop_reason
Reason why sampling stopped
tokens
List of generated token IDs
logprobs
Log probabilities for each token (optional)
tinker.types.try_again_response
TryAgainResponse Objects
class TryAgainResponse(BaseModel)
request_id
Request ID that is still pending
tinker.types.load_weights_request
LoadWeightsRequest Objects
class LoadWeightsRequest(StrictBase)
path
A tinker URI for model weights at a specific step
optimizer
Whether to load optimizer state along with model weights
tinker.types.telemetry_send_request
TelemetrySendRequest Objects
class TelemetrySendRequest(StrictBase)
platform
Host platform name
sdk_version
SDK version string
tinker.types.image_asset_pointer_chunk
ImageAssetPointerChunk Objects
class ImageAssetPointerChunk(StrictBase)
format
Image format
height
Image height in pixels
location
Path or URL to the image asset
tokens
Number of tokens this image represents
width
Image width in pixels
tinker.types.checkpoints_list_response
CheckpointsListResponse Objects
class CheckpointsListResponse(BaseModel)
checkpoints
List of available model checkpoints for the model
cursor
Pagination cursor information (None for unpaginated responses)
tinker.types.generic_event
GenericEvent Objects
class GenericEvent(BaseModel)
event
Telemetry event type
event_name
Low-cardinality event name
severity
Log severity level
event_data
Arbitrary structured JSON payload
tinker.types.encoded_text_chunk
EncodedTextChunk Objects
class EncodedTextChunk(StrictBase)
tokens
Array of token IDs
tinker.types.forward_backward_input
ForwardBackwardInput Objects
class ForwardBackwardInput(StrictBase)
data
Array of input data for the forward/backward pass
loss_fn
Fully qualified function path for the loss function
loss_fn_config
Optional configuration parameters for the loss function (e.g., PPO clip thresholds, DPO beta)
tinker.types.session_start_event
SessionStartEvent Objects
class SessionStartEvent(BaseModel)
event
Telemetry event type
severity
Log severity level
tinker.types.training_runs_response
TrainingRunsResponse Objects
class TrainingRunsResponse(BaseModel)
training_runs
List of training runs
cursor
Pagination cursor information
tinker.types.save_weights_response
SaveWeightsResponse Objects
class SaveWeightsResponse(BaseModel)
path
A tinker URI for model weights at a specific step
tinker.types.sample_request
SampleRequest Objects
class SampleRequest(StrictBase)
num_samples
Number of samples to generate
base_model
Optional base model name to sample from.
Is inferred from model_path, if provided. If sampling against a base model, this is required.
model_path
Optional tinker:// path to your model weights or LoRA weights.
If not provided, samples against the base model.
sampling_session_id
Optional sampling session ID to use instead of model_path/base_model.
If provided along with seq_id, the model configuration will be loaded from the sampling session. This is useful for multi-turn conversations.
seq_id
Sequence ID within the sampling session.
Required when sampling_session_id is provided. Used to generate deterministic request IDs for the sampling request.
prompt_logprobs
If set to true, computes and returns logprobs on the prompt tokens.
Defaults to false.
topk_prompt_logprobs
If set to a positive integer, returns the top-k logprobs for each prompt token.
tinker.types.forward_backward_output
ForwardBackwardOutput Objects
class ForwardBackwardOutput(BaseModel)
loss_fn_output_type
The type of the ForwardBackward output. Can be one of [...] TODO
loss_fn_outputs
Dictionary mapping field names to tensor data
metrics
Training metrics as key-value pairs
tinker.types.sample_response
SampleResponse Objects
class SampleResponse(BaseModel)
prompt_logprobs
If prompt_logprobs was set to true in the request, logprobs are computed for
every token in the prompt. The prompt_logprobs response contains a float32
value for every token in the prompt.
topk_prompt_logprobs
If topk_prompt_logprobs was set to a positive integer k in the request,
the top-k logprobs are computed for every token in the prompt. The
topk_prompt_logprobs response contains, for every token in the prompt,
a list of up to k (token_id, logprob) tuples.
tinker.types.create_sampling_session_response
CreateSamplingSessionResponse Objects
class CreateSamplingSessionResponse(BaseModel)
sampling_session_id
The generated sampling session ID
tinker.types.cursor
Cursor Objects
class Cursor(BaseModel)
offset
The offset used for pagination
limit
The maximum number of items requested
total_count
The total number of items available
tinker.types.create_model_request
CreateModelRequest Objects
class CreateModelRequest(StrictBase)
base_model
Optional metadata about this model/training run, set by the end-user
tinker.types.datum
Datum Objects
class Datum(StrictBase)
loss_fn_inputs
Dictionary mapping field names to tensor data
convert_tensors
@model_validator(mode="before")
@classmethod
def convert_tensors(cls, data: Any) -> Any
Convert torch.Tensor and numpy arrays to TensorData in loss_fn_inputs during construction.
tinker.types.training_run
TrainingRun Objects
class TrainingRun(BaseModel)
training_run_id
The unique identifier for the training run
base_model
The base model name this model is derived from
model_owner
The owner/creator of this model
is_lora
Whether this model uses LoRA (Low-Rank Adaptation)
corrupted
Whether the model is in a corrupted state
lora_rank
The LoRA rank if this is a LoRA model, null otherwise
last_request_time
The timestamp of the last request made to this model
last_checkpoint
The most recent training checkpoint, if available
last_sampler_checkpoint
The most recent sampler checkpoint, if available
user_metadata
Optional metadata about this training run, set by the end-user
tinker.types.session_end_event
SessionEndEvent Objects
class SessionEndEvent(BaseModel)
duration
ISO 8601 duration string
event
Telemetry event type
severity
Log severity level
tinker.types.telemetry_batch
TelemetryBatch Objects
class TelemetryBatch(BaseModel)
platform
Host platform name
sdk_version
SDK version string
tinker.types.unhandled_exception_event
UnhandledExceptionEvent Objects
class UnhandledExceptionEvent(BaseModel)
event
Telemetry event type
severity
Log severity level
traceback
Optional Python traceback string
tinker.types.image_chunk
ImageChunk Objects
class ImageChunk(StrictBase)
data
Image data as bytes
format
Image format
height
Image height in pixels
tokens
Number of tokens this image represents
width
Image width in pixels
expected_tokens
Expected number of tokens this image represents. This is only advisory: the tinker backend will compute the number of tokens from the image, and we can fail requests quickly if the tokens does not match expected_tokens.
validate_data
@field_validator("data", mode="before")
@classmethod
def validate_data(cls, value: Union[bytes, str]) -> bytes
Deserialize base64 string to bytes if needed.
serialize_data
@field_serializer("data")
def serialize_data(value: bytes) -> str
Serialize bytes to base64 string for JSON.
tinker.types.save_weights_request
SaveWeightsRequest Objects
class SaveWeightsRequest(StrictBase)
path
A file/directory name for the weights
tinker.types.lora_config
LoraConfig Objects
class LoraConfig(StrictBase)
rank
LoRA rank (dimension of low-rank matrices)
seed
Seed used for initialization of LoRA weights.
Useful if you need deterministic or reproducible initialization of weights.
train_unembed
Whether to add lora to the unembedding layer
train_mlp
Whether to add loras to the MLP layers (including MoE layers)
train_attn
Whether to add loras to the attention layers
tinker.types.create_sampling_session_request
CreateSamplingSessionRequest Objects
class CreateSamplingSessionRequest(StrictBase)
session_id
The session ID to create the sampling session within
sampling_session_seq_id
Sequence ID for the sampling session within the session
base_model
Optional base model name to sample from.
Is inferred from model_path, if provided. If sampling against a base model, this is required.
model_path
Optional tinker:// path to your model weights or LoRA weights.
If not provided, samples against the base model.
tinker.types.future_retrieve_request
FutureRetrieveRequest Objects
class FutureRetrieveRequest(StrictBase)
request_id
The ID of the request to retrieve
tinker.types.tensor_data
TensorData Objects
class TensorData(StrictBase)
data
Flattened tensor data as array of numbers.
shape
Optional.
The shape of the tensor (see PyTorch tensor.shape). The shape of a
one-dimensional list of length N is (N,). Can usually be inferred if not
provided, and is generally inferred as a 1D tensor.
to_numpy
def to_numpy() -> npt.NDArray[Any]
Convert TensorData to numpy array.
to_torch
def to_torch() -> "torch.Tensor"
Convert TensorData to torch tensor.
tinker.types.save_weights_for_sampler_request
SaveWeightsForSamplerRequest Objects
class SaveWeightsForSamplerRequest(StrictBase)
path
A file/directory name for the weights
tinker.types.sampling_params
SamplingParams Objects
class SamplingParams(BaseModel)
max_tokens
Maximum number of tokens to generate
seed
Random seed for reproducible generation
stop
Stop sequences for generation
temperature
Sampling temperature
top_k
Top-k sampling parameter (-1 for no limit)
top_p
Nucleus sampling probability
tinker.types.save_weights_for_sampler_response
SaveWeightsForSamplerResponseInternal Objects
class SaveWeightsForSamplerResponseInternal(BaseModel)
path
A tinker URI for model weights for sampling at a specific step
sampling_session_id
The generated sampling session ID
SaveWeightsForSamplerResponse Objects
class SaveWeightsForSamplerResponse(BaseModel)
path
A tinker URI for model weights for sampling at a specific step
tinker.types.load_weights_response
LoadWeightsResponse Objects
class LoadWeightsResponse(BaseModel)
path
A tinker URI for model weights at a specific step