Parameters and Types

Core data types used throughout the MinT API

AdamParams

Configuration for Adam optimizer.

Fields:

Field	Type	Default	Description
`learning_rate`	float	0.0001	Step size for parameter updates
`beta1`	float	0.9	Exponential decay rate for first moment estimates
`beta2`	float	0.95	Exponential decay rate for second moment estimates
`eps`	float	1e-12	Small constant for numerical stability

Example:

from mint import types
 
training_client.optim_step(
    types.AdamParams(learning_rate=1e-4, beta1=0.9, beta2=0.95)
).result()

ModelInput

Represents tokenized input to the model.

Methods:

from_ints(tokens) - Create from list of token IDs
to_ints() - Convert to list of token IDs
context_length() - Get total number of tokens
append_tokens(tokens) - Add tokens to the end
append_chunks(chunks) - Add text chunks

Supports:

Pure text (list of token IDs)

SamplingParams

Controls text generation behavior.

Fields:

Field	Type	Default	Description
`max_tokens`	int	required	Maximum tokens to generate
`temperature`	float	1.0	Randomness (0.0 = deterministic, higher = more random)
`top_k`	int	-1	Consider only top-k tokens (-1 = disabled)
`top_p`	float	1.0	Nucleus sampling threshold
`seed`	int	None	Random seed for reproducibility
`stop`	list	None	Stop sequences (strings or token IDs)
`stop_token_ids`	list	None	Token IDs that halt generation

Example:

from mint import types
 
result = sampling_client.sample(
    prompt=types.ModelInput.from_ints(tokens),
    num_samples=4,
    sampling_params=types.SamplingParams(
        max_tokens=100,
        temperature=0.8,
        top_p=0.95,
        stop_token_ids=[tokenizer.eos_token_id]
    )
).result()

LoraConfig

Configuration for LoRA adaptation.

Fields:

Field	Type	Default	Description
`rank`	int	32	Rank dimension for low-rank matrices (max: 64)
`seed`	int	None	Initialization seed
`train_unembed`	bool	true	Train unembedding layer
`train_mlp`	bool	true	Train MLP layers
`train_attn`	bool	true	Train attention layers

Example:

training_client = service_client.create_lora_training_client(
    base_model="Qwen/Qwen3-4B-Instruct-2507",
    rank=16,
    train_mlp=True,
    train_attn=True,
    train_unembed=True
)

Checkpoint

Represents a saved model checkpoint.

Fields:

id - Unique identifier
type - “training” or “sampler”
timestamp - Creation time
size - File size in bytes
public - Whether publicly accessible

TensorData

Wrapper for tensor data with conversion utilities.

Methods:

to_numpy() - Convert to NumPy array
to_torch() - Convert to PyTorch tensor
shape - Get tensor dimensions

TrainingRun

Metadata about a training session.

Fields:

id - Unique identifier
base_model - Foundation model used
owner - User who created the run
corrupted - Whether run encountered errors
checkpoints - List of saved checkpoints
created_at - Timestamp

APIFuture Exceptions