List fine-tuning checkpoints

fine_tuning.jobs.checkpoints.list(, ) -> SyncCursorPage[FineTuningJobCheckpoint]

GET/fine_tuning/jobs/{fine_tuning_job_id}/checkpoints

List checkpoints for a fine-tuning job.

ParametersExpand Collapse

fine_tuning_job_id: str

after: Optional[str]

Identifier for the last checkpoint ID from the previous pagination request.

limit: Optional[int]

Number of checkpoints to retrieve.

ReturnsExpand Collapse

class FineTuningJobCheckpoint: …

The fine_tuning.job.checkpoint object represents a model checkpoint for a fine-tuning job that is ready to use.

id: str

The checkpoint identifier, which can be referenced in the API endpoints.

created_at: int

The Unix timestamp (in seconds) for when the checkpoint was created.

fine_tuned_model_checkpoint: str

The name of the fine-tuned checkpoint model that is created.

fine_tuning_job_id: str

The name of the fine-tuning job that this checkpoint was created from.

metrics: Metrics

Metrics at the step number during the fine-tuning job.

full_valid_loss: Optional[float]

full_valid_mean_token_accuracy: Optional[float]

step: Optional[float]

train_loss: Optional[float]

train_mean_token_accuracy: Optional[float]

valid_loss: Optional[float]

valid_mean_token_accuracy: Optional[float]

object: Literal["fine_tuning.job.checkpoint"]

The object type, which is always "fine_tuning.job.checkpoint".

step_number: int

The step number that the checkpoint was created at.

List fine-tuning checkpoints

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ.get("OPENAI_API_KEY"),  # This is the default and can be omitted
)
page = client.fine_tuning.jobs.checkpoints.list(
    fine_tuning_job_id="ft-AF1WoRqd3aJAHsqc9NY7iL8F",
)
page = page.data[0]
print(page.id)

{
  "object": "list",
  "data": [
    {
      "object": "fine_tuning.job.checkpoint",
      "id": "ftckpt_zc4Q7MP6XxulcVzj4MZdwsAB",
      "created_at": 1721764867,
      "fine_tuned_model_checkpoint": "ft:gpt-4o-mini-2024-07-18:my-org:custom-suffix:96olL566:ckpt-step-2000",
      "metrics": {
        "full_valid_loss": 0.134,
        "full_valid_mean_token_accuracy": 0.874
      },
      "fine_tuning_job_id": "ftjob-abc123",
      "step_number": 2000
    },
    {
      "object": "fine_tuning.job.checkpoint",
      "id": "ftckpt_enQCFmOTGj3syEpYVhBRLTSy",
      "created_at": 1721764800,
      "fine_tuned_model_checkpoint": "ft:gpt-4o-mini-2024-07-18:my-org:custom-suffix:7q8mpxmy:ckpt-step-1000",
      "metrics": {
        "full_valid_loss": 0.167,
        "full_valid_mean_token_accuracy": 0.781
      },
      "fine_tuning_job_id": "ftjob-abc123",
      "step_number": 1000
    }
  ],
  "first_id": "ftckpt_zc4Q7MP6XxulcVzj4MZdwsAB",
  "last_id": "ftckpt_enQCFmOTGj3syEpYVhBRLTSy",
  "has_more": true
}

Returns Examples

{
  "object": "list",
  "data": [
    {
      "object": "fine_tuning.job.checkpoint",
      "id": "ftckpt_zc4Q7MP6XxulcVzj4MZdwsAB",
      "created_at": 1721764867,
      "fine_tuned_model_checkpoint": "ft:gpt-4o-mini-2024-07-18:my-org:custom-suffix:96olL566:ckpt-step-2000",
      "metrics": {
        "full_valid_loss": 0.134,
        "full_valid_mean_token_accuracy": 0.874
      },
      "fine_tuning_job_id": "ftjob-abc123",
      "step_number": 2000
    },
    {
      "object": "fine_tuning.job.checkpoint",
      "id": "ftckpt_enQCFmOTGj3syEpYVhBRLTSy",
      "created_at": 1721764800,
      "fine_tuned_model_checkpoint": "ft:gpt-4o-mini-2024-07-18:my-org:custom-suffix:7q8mpxmy:ckpt-step-1000",
      "metrics": {
        "full_valid_loss": 0.167,
        "full_valid_mean_token_accuracy": 0.781
      },
      "fine_tuning_job_id": "ftjob-abc123",
      "step_number": 1000
    }
  ],
  "first_id": "ftckpt_zc4Q7MP6XxulcVzj4MZdwsAB",
  "last_id": "ftckpt_enQCFmOTGj3syEpYVhBRLTSy",
  "has_more": true
}