## Best fine-tune launch config for re-launch parity

**get** `/api/v1/datasets/{dataset_id}/finetune/best-launch-config`

Returns the persisted launch snapshot for the winning AutoScientist job (terminal experiment with `best_finetune_job_id`) or the newest succeeded most recent succeeded standalone job when no experiment exists. Matches the launch payload plus `original_model_name` and persisted `trained_model_name` (output label / suffix). Use it to prefill a re-launch instead of reading config from GET /datasets/{dataset_id} or the list.

### Path Parameters

- `dataset_id: string`

### Returns

- `best_job_config: object { finetune_job_id, training_experiment_id, original_model_name, 5 more }`

  Launch-parity snapshot for the experiment best job (terminal experiment with `best_finetune_job_id`) or, when no experiment exists, the newest succeeded standalone job (`training_experiment_id` null). Null while an experiment is non-terminal, when no best job was chosen yet, or when no qualifying job exists.

  - `finetune_job_id: string`

    Fine-tune job whose config is shown (experiment best or standalone).

  - `training_experiment_id: unknown`

    Training experiment when this snapshot is the AutoScientist best job; null for a standalone job.

  - `original_model_name: string`

    Base model id the job was launched with.

  - `trained_model_name: string`

    Output label / suffix for the trained model. Taken from the value recorded at launch when present; otherwise derived from the current dataset name, in which case it can differ from the label the job was submitted with if the dataset was renamed. Null only when no label was recorded and the dataset is unavailable.

  - `training_method: "sft" or "dpo"`

    - `"sft"`

    - `"dpo"`

  - `training_type: "lora" or "full"`

    - `"lora"`

    - `"full"`

  - `data_format: "chat" or "instruction" or "preference"`

    - `"chat"`

    - `"instruction"`

    - `"preference"`

  - `hyperparams: map[unknown]`

    Hyperparameters the job was launched with.

### Example

```http
curl https://api.prod.adaptionlabs.ai/api/v1/datasets/$DATASET_ID/finetune/best-launch-config \
    -H "Authorization: Bearer $ADAPTION_API_KEY"
```

#### Response

```json
{
  "best_job_config": {
    "finetune_job_id": "182bd5e5-6e1a-4fe4-a799-aa6d9a6ab26e",
    "training_experiment_id": {},
    "original_model_name": "original_model_name",
    "trained_model_name": "trained_model_name",
    "training_method": "sft",
    "training_type": "lora",
    "data_format": "chat",
    "hyperparams": {
      "foo": "bar"
    }
  }
}
```