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Supervised fine-tuning ​

Fine-tune models with example inputs and known good outputs.

Supervised fine-tuning (SFT) lets you teach an OpenAI model to better handle your specific use cases by training it on examples you provide. The result is a customized model that more reliably produces your desired style and content.

Fine-tuning a model this way has four major parts:

  1. Build your training dataset to determine what "good" looks like
  2. Upload a training dataset containing example prompts and desired model output
  3. Create a fine-tuning job for a base model using your training data
  4. Evaluate your results using the fine-tuned model

Do not make the investment of fine-tuning models without good evals already in place! You need a reliable way to determine whether your fine-tuned model is performing better than a base model.

Set up evals →

Provide example data and create a fine-tuning job to optimize model performance for your use case

Build your dataset ​

Build a robust, representative dataset to get useful results from a fine-tuned model. Use the following techniques and considerations.

Right number of examples ​

  • The minimum number of examples you can provide for fine-tuning is 10
  • We see improvements from fine-tuning on 50–100 examples, but the right number for you varies greatly and depends on the use case
  • We recommend starting with 50 well-crafted demonstrations and evaluating the results

If performance improves with 50 good examples, try adding examples to see further results. If 50 examples have no impact, rethink your task or prompt before adding training data.

What makes a good example ​

  • Whatever prompts and outputs you expect in your application, as realistic as possible
  • Specific, clear questions and answers
  • Use historical data, expert data, logged data, or other types of collected data

Formatting your data ​

JSONL format example file

An example of JSONL training data, where the model calls a get_weather function:

text
{"messages":[{"role":"user","content":"What is the weather in San Francisco?"},{"role":"assistant","tool_calls":[{"id":"call_id","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\": \"San Francisco, USA\", \"format\": \"celsius\"}"}}]}],"parallel_tool_calls":false,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and country, eg. San Francisco, USA"},"format":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location","format"]}}}]}
{"messages":[{"role":"user","content":"What is the weather in Minneapolis?"},{"role":"assistant","tool_calls":[{"id":"call_id","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\": \"Minneapolis, USA\", \"format\": \"celsius\"}"}}]}],"parallel_tool_calls":false,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and country, eg. Minneapolis, USA"},"format":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location","format"]}}}]}
{"messages":[{"role":"user","content":"What is the weather in San Diego?"},{"role":"assistant","tool_calls":[{"id":"call_id","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\": \"San Diego, USA\", \"format\": \"celsius\"}"}}]}],"parallel_tool_calls":false,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and country, eg. San Diego, USA"},"format":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location","format"]}}}]}
{"messages":[{"role":"user","content":"What is the weather in Memphis?"},{"role":"assistant","tool_calls":[{"id":"call_id","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\": \"Memphis, USA\", \"format\": \"celsius\"}"}}]}],"parallel_tool_calls":false,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and country, eg. Memphis, USA"},"format":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location","format"]}}}]}
{"messages":[{"role":"user","content":"What is the weather in Atlanta?"},{"role":"assistant","tool_calls":[{"id":"call_id","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\": \"Atlanta, USA\", \"format\": \"celsius\"}"}}]}],"parallel_tool_calls":false,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and country, eg. Atlanta, USA"},"format":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location","format"]}}}]}
{"messages":[{"role":"user","content":"What is the weather in Sunnyvale?"},{"role":"assistant","tool_calls":[{"id":"call_id","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\": \"Sunnyvale, USA\", \"format\": \"celsius\"}"}}]}],"parallel_tool_calls":false,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and country, eg. Sunnyvale, USA"},"format":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location","format"]}}}]}
{"messages":[{"role":"user","content":"What is the weather in Chicago?"},{"role":"assistant","tool_calls":[{"id":"call_id","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\": \"Chicago, USA\", \"format\": \"celsius\"}"}}]}],"parallel_tool_calls":false,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and country, eg. Chicago, USA"},"format":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location","format"]}}}]}
{"messages":[{"role":"user","content":"What is the weather in Boston?"},{"role":"assistant","tool_calls":[{"id":"call_id","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\": \"Boston, USA\", \"format\": \"celsius\"}"}}]}],"parallel_tool_calls":false,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and country, eg. Boston, USA"},"format":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location","format"]}}}]}
{"messages":[{"role":"user","content":"What is the weather in Honolulu?"},{"role":"assistant","tool_calls":[{"id":"call_id","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\": \"Honolulu, USA\", \"format\": \"celsius\"}"}}]}],"parallel_tool_calls":false,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and country, eg. Honolulu, USA"},"format":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location","format"]}}}]}
{"messages":[{"role":"user","content":"What is the weather in San Antonio?"},{"role":"assistant","tool_calls":[{"id":"call_id","type":"function","function":{"name":"get_current_weather","arguments":"{\"location\": \"San Antonio, USA\", \"format\": \"celsius\"}"}}]}],"parallel_tool_calls":false,"tools":[{"type":"function","function":{"name":"get_current_weather","description":"Get the current weather","parameters":{"type":"object","properties":{"location":{"type":"string","description":"The city and country, eg. San Antonio, USA"},"format":{"type":"string","enum":["celsius","fahrenheit"]}},"required":["location","format"]}}}]}

Corresponding JSON data

Each line of the training data file contains a JSON structure like the following, containing both an example user prompt and a correct response from the model as an assistant message.

json
{
  "messages": [
    { "role": "user", "content": "What is the weather in San Francisco?" },
    {
      "role": "assistant",
      "tool_calls": [
        {
          "id": "call_id",
          "type": "function",
          "function": {
            "name": "get_current_weather",
            "arguments": "{\"location\": \"San Francisco, USA\", \"format\": \"celsius\"}"
          }
        }
      ]
    }
  ],
  "parallel_tool_calls": false,
  "tools": [
    {
      "type": "function",
      "function": {
        "name": "get_current_weather",
        "description": "Get the current weather",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
                "type": "string",
                "description": "The city and country, eg. San Francisco, USA"
            },
            "format": { "type": "string", "enum": ["celsius", "fahrenheit"] }
          },
          "required": ["location", "format"]
        }
      }
    }
  ]
}

Upload training data ​

Upload your dataset of examples to OpenAI. We use it to update the model's weights and produce outputs like the ones included in your data.

In addition to text completions, you can train the model to more effectively generate structured JSON output or function calls.

Upload your data with button clicks

  1. Navigate to the dashboard > fine-tuning.
  2. Click + Create.
  3. Under Training data, upload your JSONL file.

Call the API to upload your data

Assuming the data above is saved to a file called mydata.jsonl, you can upload it to the OpenAI platform using the code below. Note that the purpose of the uploaded file is set to fine-tune:

bash
curl https://api.openai.com/v1/files \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -F purpose="fine-tune" \
  -F file="@mydata.jsonl"

Note the id of the file that is uploaded in the data returned from the API - you'll need that file identifier in subsequent API requests.

json
{
  "object": "file",
  "id": "file-RCnFCYRhFDcq1aHxiYkBHw",
  "purpose": "fine-tune",
  "filename": "mydata.jsonl",
  "bytes": 1058,
  "created_at": 1746484901,
  "expires_at": null,
  "status": "processed",
  "status_details": null
}

Create a fine-tuning job ​

With your test data uploaded, create a fine-tuning job to customize a base model using the training data you provide. When creating a fine-tuning job, you must specify:

  • A base model (model) to use for fine-tuning. This can be either an OpenAI model ID or the ID of a previously fine-tuned model. See which models support fine-tuning in the model docs.
  • A training file (training_file) ID. This is the file you uploaded in the previous step.
  • A fine-tuning method (method). This specifies which fine-tuning method you want to use to customize the model. Supervised fine-tuning is the default.

Upload your data with button clicks

  1. In the same + Create modal as above, complete the required fields.
  2. Select supervised fine-tuning as the method and whichever model you want to train.
  3. When you're ready, click Create to start the job.

Call the API to upload your data

Create a supervised fine-tuning job by calling the fine-tuning API:

bash
curl https://api.openai.com/v1/fine_tuning/jobs \                               
  -H "Content-Type: application/json" \     
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "training_file": "file-RCnFCYRhFDcq1aHxiYkBHw",
    "model": "gpt-4.1-nano-2025-04-14"
  }'

The API responds with information about the fine-tuning job in progress. Depending on the size of your training data, the training process may take several minutes or hours. You can poll the API for updates on a specific job.

When the fine-tuning job finishes, your fine-tuned model is ready to use. A completed fine-tune job returns data like this:

json
{
  "object": "fine_tuning.job",
  "id": "ftjob-uL1VKpwx7maorHNbOiDwFIn6",
  "model": "gpt-4.1-nano-2025-04-14",
  "created_at": 1746484925,
  "finished_at": 1746485841,
  "fine_tuned_model": "ft:gpt-4.1-nano-2025-04-14:openai::BTz2REMH",
  "organization_id": "org-abc123",
  "result_files": [
    "file-9TLxKY2A8tC5YE1RULYxf6"
  ],
  "status": "succeeded",
  "validation_file": null,
  "training_file": "file-RCnFCYRhFDcq1aHxiYkBHw",
  "hyperparameters": {
    "n_epochs": 10,
    "batch_size": 1,
    "learning_rate_multiplier": 1
  },
  "trained_tokens": 1700,
  "error": {},
  "user_provided_suffix": null,
  "seed": 1935755117,
  "estimated_finish": null,
  "integrations": [],
  "metadata": null,
  "usage_metrics": null,
  "shared_with_openai": false,
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "n_epochs": 10,
        "batch_size": 1,
        "learning_rate_multiplier": 1.0
      }
    }
  }
}

Note the fine_tuned_model property. This is the model ID to use in Responses or Chat Completions to make API requests using your fine-tuned model.

Here's an example of calling the Responses API with your fine-tuned model ID:

bash
curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "ft:gpt-4.1-nano-2025-04-14:openai::BTz2REMH",
    "input": "What is the weather like in Boston today?",
    "tools": [
      {
        "name": "get_current_weather",
        "description": "Get the current weather",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
                "type": "string",
                "description": "The city and country, eg. San Francisco, USA"
            },
            "format": { "type": "string", "enum": ["celsius", "fahrenheit"] }
          },
          "required": ["location", "format"]
        }
      }
    ],
    "tool_choice": "auto"
  }'

Evaluate the result ​

Use the approaches below to check how your fine-tuned model performs. Adjust your prompts, data, and fine-tuning job as needed until you get the results you want. The best way to fine-tune is to continue iterating.

Compare to evals ​

To see if your fine-tuned model performs better than the original base model, use evals. Before running your fine-tuning job, carve out data from the same training dataset you collected in step 1. This holdout data acts as a control group when you use it for evals. Make sure the training and holdout data have roughly the same diversity of user input types and model responses.

Learn more about running evals.

Monitor the status ​

Check the status of a fine-tuning job in the dashboard or by polling the job ID in the API.

Monitor in the UI

  1. Navigate to the fine-tuning dashboard.
  2. Select the job you want to monitor.
  3. Review the status, checkpoints, message, and metrics.

Monitor with API calls

Use this curl command to get information about your fine-tuning job:

bash
curl https://api.openai.com/v1/fine_tuning/jobs/ftjob-uL1VKpwx7maorHNbOiDwFIn6 \
  -H "Authorization: Bearer $OPENAI_API_KEY"

The job contains a fine_tuned_model property, which is your new fine-tuned model's unique ID.

json
{
  "object": "fine_tuning.job",
  "id": "ftjob-uL1VKpwx7maorHNbOiDwFIn6",
  "model": "gpt-4.1-nano-2025-04-14",
  "created_at": 1746484925,
  "finished_at": 1746485841,
  "fine_tuned_model": "ft:gpt-4.1-nano-2025-04-14:openai::BTz2REMH",
  "organization_id": "org-abc123",
  "result_files": [
    "file-9TLxKY2A8tC5YE1RULYxf6"
  ],
  "status": "succeeded",
  "validation_file": null,
  "training_file": "file-RCnFCYRhFDcq1aHxiYkBHw",
  "hyperparameters": {
    "n_epochs": 10,
    "batch_size": 1,
    "learning_rate_multiplier": 1
  },
  "trained_tokens": 1700,
  "error": {},
  "user_provided_suffix": null,
  "seed": 1935755117,
  "estimated_finish": null,
  "integrations": [],
  "metadata": null,
  "usage_metrics": null,
  "shared_with_openai": false,
  "method": {
    "type": "supervised",
    "supervised": {
      "hyperparameters": {
        "n_epochs": 10,
        "batch_size": 1,
        "learning_rate_multiplier": 1.0
      }
    }
  }
}

Try using your fine-tuned model ​

Evaluate your newly optimized model by using it! When the fine-tuned model finishes training, use its ID in either the Responses or Chat Completions API, just as you would an OpenAI base model.

Use your model in the Playground

  1. Navigate to your fine-tuning job in the dashboard.
  2. In the right pane, navigate to Output model and copy the model ID. It should start with ft:…
  3. Open the Playground.
  4. In the Model dropdown menu, paste the model ID. Here, you should also see other fine-tuned models you've created.
  5. Run some prompts and see how your fine-tuned performs!

Use your model with an API call

bash
curl https://api.openai.com/v1/responses \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "ft:gpt-4.1-nano-2025-04-14:openai::BTz2REMH",
    "input": "What is 4+4?"
  }'

Use checkpoints if needed ​

Checkpoints are models you can use. We create a full model checkpoint for you at the end of each training epoch. They're useful in cases where your fine-tuned model improves early on but then memorizes the dataset instead of learning generalizable knowledge—called _overfitting. Checkpoints provide versions of your customized model from various moments in the process.

Find checkpoints in the dashboard

  1. Navigate to the fine-tuning dashboard.
  2. In the left panel, select the job you want to investigate. Wait until it succeeds.
  3. In the right panel, scroll to the list of checkpoints.
  4. Hover over any checkpoint to see a link to launch in the Playground.
  5. Test the checkpoint model's behavior by prompting it in the Playground.

Query the API for checkpoints

  1. Wait until a job succeeds, which you can verify by querying the status of a job.
  2. Query the checkpoints endpoint with your fine-tuning job ID to access a list of model checkpoints for the fine-tuning job.
  3. Find the fine_tuned_model_checkpoint field for the name of the model checkpoint.
  4. Use this model just like you would the final fine-tuned model.

The checkpoint object contains metrics data to help you determine the usefulness of this model. As an example, the response looks like this:

json
{
  "object": "fine_tuning.job.checkpoint",
  "id": "ftckpt_zc4Q7MP6XxulcVzj4MZdwsAB",
  "created_at": 1519129973,
  "fine_tuned_model_checkpoint": "ft:gpt-3.5-turbo-0125: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
}

Each checkpoint specifies:

  • step_number: The step at which the checkpoint was created (where each epoch is number of steps in the training set divided by the batch size)
  • metrics: An object containing the metrics for your fine-tuning job at the step when the checkpoint was created

Currently, only the checkpoints for the last three epochs of the job are saved and available for use.

Next steps ​

Now that you know the basics of supervised fine-tuning, explore these other methods as well.

[

Vision fine-tuning

Learn to fine-tune for computer vision with image inputs.

](/docs/openai/guides/vision-fine-tuning)[

Direct preference optimization

Fine-tune a model using direct preference optimization (DPO).

](/docs/openai/guides/direct-preference-optimization)[

Reinforcement fine-tuning

Fine-tune a reasoning model by grading its outputs.

](/docs/openai/guides/reinforcement-fine-tuning)

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