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List fine-tuning checkpoints

client.fineTuning.jobs.checkpoints.list(, ?, ?): CursorPage<FineTuningJobCheckpoint { id, created_at, fine_tuned_model_checkpoint, 4 more } >

GET/fine_tuning/jobs/{fine_tuning_job_id}/checkpoints

List checkpoints for a fine-tuning job.

ParametersExpand Collapse

fineTuningJobID: string

query: CheckpointListParams { after, limit }

after?: string

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

limit?: number

Number of checkpoints to retrieve.

ReturnsExpand Collapse

FineTuningJobCheckpoint { id, created_at, fine_tuned_model_checkpoint, 4 more }

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

id: string

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

created_at: number

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

fine_tuned_model_checkpoint: string

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

fine_tuning_job_id: string

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

metrics: Metrics { full_valid_loss, full_valid_mean_token_accuracy, step, 4 more }

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

full_valid_loss?: number

full_valid_mean_token_accuracy?: number

step?: number

train_loss?: number

train_mean_token_accuracy?: number

valid_loss?: number

valid_mean_token_accuracy?: number

object: "fine_tuning.job.checkpoint"

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

step_number: number

The step number that the checkpoint was created at.

List fine-tuning checkpoints

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env['OPENAI_API_KEY'], // This is the default and can be omitted
});

// Automatically fetches more pages as needed.
for await (const fineTuningJobCheckpoint of client.fineTuning.jobs.checkpoints.list(
  'ft-AF1WoRqd3aJAHsqc9NY7iL8F',
)) {
  console.log(fineTuningJobCheckpoint.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
}