Recommend hyperparameters

autoscientist.recommend_hyperparams() -> AutoscientistRecommendHyperparamsResponse

POST/api/v1/autoscientist/recommend-hyperparams

Returns the hyperparameters AutoScientist derives for a model and the dataset's effective training size (rows plus planned augmentation). Nothing is launched. Omit model to size against the model POST /autoscientist would select for this dataset; the resolved id is returned with the values. POST /autoscientist applies the same values when hyperparams is omitted.

ParametersExpand Collapse

dataset_id: str

The dataset the hyperparameters are sized for. Must be a dataset you own that has finished processing.

model: Optional[str]

Base model id from GET /autoscientist/models to tune for. Omit to size the recommendation against the model POST /autoscientist selects for this dataset; the resolved id is returned in the response model field. That pick reads the columns already selected for the dataset, so a create request supplying column_mapping.reasoning_trace can resolve a different model. Pass the returned model back on create to pin it.

augmentation_domain_rows: Optional[float]

Number of synthetic domain-targeted rows you plan to generate. These are additional to your own rows and count toward the effective training size, so the recommendation matches what you will launch. Defaults to 0 (no augmentation).

minimum0

maximum40000

augmentation_general_rows: Optional[float]

Number of synthetic general-diversity rows you plan to generate. These are additional to your own rows and count toward the effective training size. Defaults to 0 (no augmentation).

minimum0

maximum50000

ReturnsExpand Collapse

class AutoscientistRecommendHyperparamsResponse: …

model: str

Resolved base model id the recommendation is sized for: the value you sent, or the automatically selected model when model was omitted. Pass it back as model on POST /autoscientist to launch against the same model.

hyperparams: Hyperparams

Hyperparameters derived for the resolved model and effective dataset size. POST /autoscientist applies the same values when hyperparams is omitted. Overriding is not advised.

n_epochs: Optional[float]

minimum1

maximum20

batch_size: Optional[Union[Literal["max"], int, null]]

Either the literal 'max' or an integer of 1 or more. No fixed upper bound: the ceiling depends on the model and hardware, and a larger value is rejected at launch. Defaults to 'max'.

One of the following:

Literal["max"]

int

learning_rate: Optional[float]

minimum1e-8

maximum0.01

lora_r: Optional[float]

minimum1

maximum64

lora_alpha: Optional[float]

Must be 1× or 2× lora_r. Only checked when you supply lora_r in the same request; otherwise it is validated against the platform default.

lora_dropout: Optional[float]

minimum0

maximum1

lora_trainable_modules: Optional[str]

'all-linear' or comma-separated module list (e.g. 'q_proj,v_proj').

lr_scheduler_type: Optional[Literal["linear", "cosine", "constant"]]

One of the following:

"linear"

"cosine"

"constant"

min_lr_ratio: Optional[float]

minimum0

maximum1

scheduler_num_cycles: Optional[float]

Number of cosine decay cycles across training. Defaults to 0.5, a single half-cycle decay from the peak learning rate down to the floor.

minimum0

maximum4

warmup_ratio: Optional[float]

minimum0

maximum1

max_grad_norm: Optional[float]

minimum0

weight_decay: Optional[float]

minimum0

train_on_inputs: Optional[bool]

Whether training loss is computed over the prompt tokens as well as the completion. true trains on the full sequence, false trains only on the completion. Omit to let the platform decide per example, which is the default.

training_type: Optional[Literal["lora", "full"]]

Training strategy override. AutoScientist optimizes this by default; set it only when strictly necessary.

One of the following:

"lora"

"full"

Recommend hyperparameters

import os
from adaption import Adaption

client = Adaption(
    api_key=os.environ.get("ADAPTION_API_KEY"),  # This is the default and can be omitted
)
response = client.autoscientist.recommend_hyperparams(
    dataset_id="dataset_id",
)
print(response.model)

{
  "model": "model",
  "hyperparams": {
    "n_epochs": 1,
    "batch_size": "max",
    "learning_rate": 0.00005,
    "lora_r": 8,
    "lora_alpha": 0,
    "lora_dropout": 0,
    "lora_trainable_modules": "q_proj,v_proj",
    "lr_scheduler_type": "linear",
    "min_lr_ratio": 0.1,
    "scheduler_num_cycles": 0.5,
    "warmup_ratio": 0.03,
    "max_grad_norm": 2,
    "weight_decay": 0,
    "train_on_inputs": false,
    "training_type": "lora"
  }
}

Returns Examples

{
  "model": "model",
  "hyperparams": {
    "n_epochs": 1,
    "batch_size": "max",
    "learning_rate": 0.00005,
    "lora_r": 8,
    "lora_alpha": 0,
    "lora_dropout": 0,
    "lora_trainable_modules": "q_proj,v_proj",
    "lr_scheduler_type": "linear",
    "min_lr_ratio": 0.1,
    "scheduler_num_cycles": 0.5,
    "warmup_ratio": 0.03,
    "max_grad_norm": 2,
    "weight_decay": 0,
    "train_on_inputs": false,
    "training_type": "lora"
  }
}