GET
/
fine-tunes
/
{id}
from together import Together
import os

client = Together(
    api_key=os.environ.get("TOGETHER_API_KEY"),
)

fine_tune = client.fine_tuning.retrieve(id="ft-id")

print(fine_tune)
{
  "id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "training_file": "<string>",
  "validation_file": "<string>",
  "model": "<string>",
  "model_output_name": "<string>",
  "model_output_path": "<string>",
  "trainingfile_numlines": 123,
  "trainingfile_size": 123,
  "created_at": "<string>",
  "updated_at": "<string>",
  "n_epochs": 123,
  "n_checkpoints": 123,
  "n_evals": 123,
  "batch_size": 123,
  "learning_rate": 123,
  "lr_scheduler": {
    "lr_scheduler_type": "linear",
    "lr_scheduler_args": {
      "min_lr_ratio": 0
    }
  },
  "warmup_ratio": 123,
  "max_grad_norm": 123,
  "weight_decay": 123,
  "eval_steps": 123,
  "train_on_inputs": true,
  "training_method": {
    "method": "sft",
    "train_on_inputs": true
  },
  "training_type": {
    "type": "Full"
  },
  "status": "pending",
  "job_id": "<string>",
  "events": [
    {
      "object": "fine-tune-event",
      "created_at": "<string>",
      "level": "info",
      "message": "<string>",
      "type": "job_pending",
      "param_count": 123,
      "token_count": 123,
      "total_steps": 123,
      "wandb_url": "<string>",
      "step": 123,
      "checkpoint_path": "<string>",
      "model_path": "<string>",
      "training_offset": 123,
      "hash": "<string>"
    }
  ],
  "token_count": 123,
  "param_count": 123,
  "total_price": 123,
  "epochs_completed": 123,
  "queue_depth": 123,
  "wandb_project_name": "<string>",
  "wandb_url": "<string>",
  "from_checkpoint": "<string>"
}

Authorizations

​
Authorization
string
header
default:default
required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Path Parameters

​
id
string
required

Response

200 - application/json

Fine-tune job details retrieved successfully

The response is of type object.