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GET
/
fine-tunes
/
{id}
# Docs for v1 can be found by changing the above selector ^
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",
  "user_id": "<string>",
  "training_file": "<string>",
  "validation_file": "<string>",
  "model": "<string>",
  "model_output_name": "<string>",
  "model_output_path": "<string>",
  "trainingfile_numlines": 123,
  "trainingfile_size": 123,
  "created_at": "2023-11-07T05:31:56Z",
  "updated_at": "2023-11-07T05:31:56Z",
  "started_at": "2023-11-07T05:31:56Z",
  "n_epochs": 123,
  "n_checkpoints": 123,
  "n_evals": 123,
  "batch_size": "max",
  "learning_rate": 123,
  "lr_scheduler": {
    "lr_scheduler_args": {
      "min_lr_ratio": 0
    }
  },
  "warmup_ratio": 123,
  "max_grad_norm": 123,
  "weight_decay": 123,
  "eval_steps": 123,
  "train_on_inputs": "auto",
  "training_method": {
    "method": "sft",
    "train_on_inputs": "auto"
  },
  "training_type": {
    "type": "Full"
  },
  "multimodal_params": {
    "train_vision": true
  },
  "job_id": "<string>",
  "events": [
    {
      "object": "<unknown>",
      "created_at": "<string>",
      "message": "<string>",
      "param_count": 123,
      "token_count": 123,
      "total_steps": 123,
      "wandb_url": "<string>",
      "step": 123,
      "checkpoint_path": "<string>",
      "model_path": "<string>",
      "early_stopping_best_step": 123,
      "early_stopping_best_metric_value": 123
    }
  ],
  "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>",
  "from_hf_model": "<string>",
  "hf_model_revision": "<string>",
  "progress": {
    "estimate_available": true,
    "seconds_remaining": 123
  },
  "early_stopped": true,
  "early_stopping_best_step": 123,
  "early_stopping_best_metric": 123
}

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

The ID of the job to retrieve

Response

200 - application/json

Fine-tune job details retrieved successfully

​
id
string<uuid>
required
​
user_id
string
required

ID of the user who owns the fine-tune job.

​
status
enum<string>
required
Available options:
pending,
queued,
running,
compressing,
uploading,
cancel_requested,
cancelled,
error,
completed
​
training_file
string
​
validation_file
string
​
model
string
​
model_output_name
string
​
model_output_path
string
​
trainingfile_numlines
integer
​
trainingfile_size
integer
​
created_at
string<date-time>
​
updated_at
string<date-time>
​
started_at
string<date-time>
​
n_epochs
integer
​
n_checkpoints
integer
​
n_evals
integer
​
batch_size
default:max
​
learning_rate
number
​
lr_scheduler
object
​
warmup_ratio
number
​
max_grad_norm
number<float>
​
weight_decay
number<float>
​
eval_steps
integer
​
train_on_inputs
default:auto
​
training_method
object
​
training_type
object
​
multimodal_params
object
​
job_id
string
​
events
object[]
​
token_count
integer
​
param_count
integer
​
total_price
integer
​
epochs_completed
integer
​
queue_depth
integer
​
wandb_project_name
string
​
wandb_url
string
​
from_checkpoint
string
​
from_hf_model
string
​
hf_model_revision
string
​
progress
object

Progress information for a fine-tuning job

​
early_stopped
boolean

Whether the early-stopping criterion triggered.

​
early_stopping_best_step
integer

Step associated with the selected early-stopping artifact. When early_stopping_best_metric is null, no finite best metric was recorded; this is the halt step, not a best-checkpoint step.

​
early_stopping_best_metric
number<float> | null

Best validation loss observed, corresponding to early_stopping_best_step. Null if no improving evaluation was recorded (for example, a non-finite first evaluation).