## Recommend hyperparameters

`autoscientist.recommend_hyperparams(AutoscientistRecommendHyperparamsParams**kwargs)  -> 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.

### Parameters

- `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).

- `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).

### Returns

- `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]`

    - `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'.

      - `Literal["max"]`

        - `"max"`

      - `int`

    - `learning_rate: Optional[float]`

    - `lora_r: Optional[float]`

    - `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]`

    - `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"]]`

      - `"linear"`

      - `"cosine"`

      - `"constant"`

    - `min_lr_ratio: Optional[float]`

    - `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.

    - `warmup_ratio: Optional[float]`

    - `max_grad_norm: Optional[float]`

    - `weight_decay: Optional[float]`

    - `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.

      - `"lora"`

      - `"full"`

### Example

```python
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)
```

#### Response

```json
{
  "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"
  }
}
```