Azure OpenAI Service lets you tailor our models to your personal datasets by using a process known as fine-tuning. This customization step lets you get more out of the service by providing:
Higher quality results than what you can get just from prompt engineering
The ability to train on more examples than can fit into a model's max request context limit.
Token savings due to shorter prompts
Lower-latency requests, particularly when using smaller models.
In contrast to few-shot learning, fine tuning improves the model by training on many more examples than can fit in a prompt, letting you achieve better results on a wide number of tasks. Because fine tuning adjusts the base model’s weights to improve performance on the specific task, you won’t have to include as many examples or instructions in your prompt. This means less text sent and fewer tokens processed on every API call, potentially saving cost, and improving request latency.
We use LoRA, or low rank approximation, to fine-tune models in a way that reduces their complexity without significantly affecting their performance. This method works by approximating the original high-rank matrix with a lower rank one, thus only fine-tuning a smaller subset of "important" parameters during the supervised training phase, making the model more manageable and efficient. For users, this makes training faster and more affordable than other techniques.
Note
Azure OpenAI currently only supports text-to-text fine-tuning for all supported models including GPT-4o mini.
The training and validation data you use must be formatted as a JSON Lines (JSONL) document. For gpt-35-turbo-0613 the fine-tuning dataset must be formatted in the conversational format that is used by the Chat completions API.
If you would like a step-by-step walk-through of fine-tuning a gpt-35-turbo-0613 model please refer to the Azure OpenAI fine-tuning tutorial.
Example file format
JSON
{"messages": [{"role": "system", "content": "You are an Xbox customer support agent whose primary goal is
to help users with issues they are experiencing with their Xbox devices. You are friendly and concise.
You only provide factual answers to queries, and do not provide answers that are not related to Xbox."},
{"role": "user", "content": "Is Xbox better than PlayStation?"},
{"role": "assistant", "content": "I apologize, but I cannot provide personal opinions.
My primary job is to assist you with any issues related to your Xbox device.
Do you have any Xbox-related issues that need addressing?"}]}
{"messages": [{"role": "system", "content": "You are an Xbox customer support agent whose primary goal is
to help users with issues they are experiencing with their Xbox devices. You are friendly and concise.
You only provide factual answers to queries, and do not provide answers that are not related to Xbox."},
{"role": "user", "content": "Can I play PC games on my Xbox?"}, {"role": "assistant", "content":
"While Xbox consoles don't natively support PC games, they can play an array of Xbox games,
many of which are also available on PC. Can I assist you with finding games compatible with your Xbox device?"}]}
{"messages": [{"role": "system", "content": "You are an Xbox customer support agent whose primary goal is
to help users with issues they are experiencing with their Xbox devices. You are friendly and concise.
You only provide factual answers to queries, and do not provide answers that are not related to Xbox."},
{"role": "user", "content": "I'm having trouble connecting my Xbox to the Wi-Fi."},
{"role": "assistant", "content": "No worries, let's go through the network settings on your Xbox.
Can you please tell me what happens when you try to connect it to the Wi-Fi?"}]}
Multi-turn chat file format
Multiple turns of a conversation in a single line of your jsonl training file is also supported. To skip fine-tuning on specific assistant messages add the optional weight key value pair. Currently weight can be set to 0 or 1.
JSON
{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."},
{"role": "user", "content": "What's the capital of France?"}, {"role": "assistant", "content":
"Paris", "weight": 0},
{"role": "user", "content": "Can you be more sarcastic?"}, {"role": "assistant", "content":
"Paris, as if everyone doesn't know that already.", "weight": 1}]}
{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."},
{"role": "user", "content": "Who wrote 'Romeo and Juliet'?"}, {"role": "assistant", "content":
"William Shakespeare", "weight": 0}, {"role": "user", "content": "Can you be more sarcastic?"},
{"role": "assistant", "content": "Oh, just some guy named William Shakespeare. Ever heard of him?", "weight": 1}]}
{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."},
{"role": "user", "content": "How far is the Moon from Earth?"},
{"role": "assistant", "content": "384,400 kilometers", "weight": 0},
{"role": "user", "content": "Can you be more sarcastic?"},
{"role": "assistant", "content": "Around 384,400 kilometers. Give or take a few, like that really matters.", "weight": 1}]}
In addition to the JSONL format, training and validation data files must be encoded in UTF-8 and include a byte-order mark (BOM). The file must be less than 512 MB in size.
Create your training and validation datasets
The more training examples you have, the better. Fine tuning jobs will not proceed without at least 10 training examples, but such a small number are not enough to noticeably influence model responses. It is best practice to provide hundreds, if not thousands, of training examples to be successful.
In general, doubling the dataset size can lead to a linear increase in model quality. But keep in mind, low quality examples can negatively impact performance. If you train the model on a large amount of internal data, without first pruning the dataset for only the highest quality examples you could end up with a model that performs much worse than expected.
Use the Create custom model wizard
Azure OpenAI Studio provides the Create custom model wizard, so you can interactively create and train a fine-tuned model for your Azure resource.
Open Azure OpenAI Studio at https://oai.azure.com/ and sign in with credentials that have access to your Azure OpenAI resource. During the sign-in workflow, select the appropriate directory, Azure subscription, and Azure OpenAI resource.
In Azure OpenAI Studio, browse to the Management > Models pane, and select Create a custom model.
The Create custom model wizard opens.
Select the base model
The first step in creating a custom model is to choose a base model. The Base model pane lets you choose a base model to use for your custom model. Your choice influences both the performance and the cost of your model.
Select the base model from the Base model type dropdown, and then select Next to continue.
You can create a custom model from one of the following available base models:
babbage-002
davinci-002
gpt-35-turbo (0613)
gpt-35-turbo (1106)
gpt-35-turbo (0125)
gpt-4 (0613)
Or you can fine tune a previously fine-tuned model, formatted as base-model.ft-{jobid}.
For more information about our base models that can be fine-tuned, see Models.
Choose your training data
The next step is to either choose existing prepared training data or upload new prepared training data to use when customizing your model. The Training data pane displays any existing, previously uploaded datasets and also provides options to upload new training data.
If your training data is already uploaded to the service, select Choose dataset.
Select the file from the list shown in the Training data pane.
To upload new training data, use one of the following options:
For large data files, we recommend that you import from an Azure Blob store. Large files can become unstable when uploaded through multipart forms because the requests are atomic and can't be retried or resumed. For more information about Azure Blob Storage, see What is Azure Blob Storage?
Note
Training data files must be formatted as JSONL files, encoded in UTF-8 with a byte-order mark (BOM). The file must be less than 512 MB in size.
Upload training data from local file
You can upload a new training dataset to the service from a local file by using one of the following methods:
Drag and drop the file into the client area of the Training data pane, and then select Upload file.
Select Browse for a file from the client area of the Training data pane, choose the file to upload from the Open dialog, and then select Upload file.
After you select and upload the training dataset, select Next to continue.
Import training data from Azure Blob store
You can import a training dataset from Azure Blob or another shared web location by providing the name and location of the file.
Enter the File name for the file.
For the File location, provide the Azure Blob URL, the Azure Storage shared access signature (SAS), or other link to an accessible shared web location.
Select Upload file to import the training dataset to the service.
After you select and upload the training dataset, select Next to continue.
Choose your validation data
The next step provides options to configure the model to use validation data in the training process. If you don't want to use validation data, you can choose Next to continue to the advanced options for the model. Otherwise, if you have a validation dataset, you can either choose existing prepared validation data or upload new prepared validation data to use when customizing your model.
The Validation data pane displays any existing, previously uploaded training and validation datasets and provides options by which you can upload new validation data.
If your validation data is already uploaded to the service, select Choose dataset.
Select the file from the list shown in the Validation data pane.
To upload new validation data, use one of the following options:
For large data files, we recommend that you import from an Azure Blob store. Large files can become unstable when uploaded through multipart forms because the requests are atomic and can't be retried or resumed.
Note
Similar to training data files, validation data files must be formatted as JSONL files, encoded in UTF-8 with a byte-order mark (BOM). The file must be less than 512 MB in size.
Upload validation data from local file
You can upload a new validation dataset to the service from a local file by using one of the following methods:
Drag and drop the file into the client area of the Validation data pane, and then select Upload file.
Select Browse for a file from the client area of the Validation data pane, choose the file to upload from the Open dialog, and then select Upload file.
After you select and upload the validation dataset, select Next to continue.
Import validation data from Azure Blob store
You can import a validation dataset from Azure Blob or another shared web location by providing the name and location of the file.
Enter the File name for the file.
For the File location, provide the Azure Blob URL, the Azure Storage shared access signature (SAS), or other link to an accessible shared web location.
Select Upload file to import the training dataset to the service.
After you select and upload the validation dataset, select Next to continue.
Configure advanced options
The Create custom model wizard shows the parameters for training your fine-tuned model on the Advanced options pane. The following parameters are available:
Name
Type
Description
batch_size
integer
The batch size to use for training. The batch size is the number of training examples used to train a single forward and backward pass. In general, we've found that larger batch sizes tend to work better for larger datasets. The default value as well as the maximum value for this property are specific to a base model. A larger batch size means that model parameters are updated less frequently, but with lower variance.
learning_rate_multiplier
number
The learning rate multiplier to use for training. The fine-tuning learning rate is the original learning rate used for pre-training multiplied by this value. Larger learning rates tend to perform better with larger batch sizes. We recommend experimenting with values in the range 0.02 to 0.2 to see what produces the best results. A smaller learning rate may be useful to avoid overfitting.
n_epochs
integer
The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
seed
integer
The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but may differ in rare cases. If a seed isn't specified, one will be generated for you
Select Default to use the default values for the fine-tuning job, or select Advanced to display and edit the hyperparameter values. When defaults are selected, we determine the correct value algorithmically based on your training data.
The Review pane of the wizard displays information about your configuration choices.
If you're ready to train your model, select Start Training job to start the fine-tuning job and return to the Models pane.
Check the status of your custom model
The Models pane displays information about your custom model in the Customized models tab. The tab includes information about the status and job ID of the fine-tune job for your custom model. When the job completes, the tab displays the file ID of the result file. You might need to select Refresh in order to see an updated status for the model training job.
After you start a fine-tuning job, it can take some time to complete. Your job might be queued behind other jobs on the system. Training your model can take minutes or hours depending on the model and dataset size.
Here are some of the tasks you can do on the Models pane:
Check the status of the fine-tuning job for your custom model in the Status column of the Customized models tab.
In the Model name column, select the model name to view more information about the custom model. You can see the status of the fine-tuning job, training results, training events, and hyperparameters used in the job.
Select Download training file to download the training data you used for the model.
Select Download results to download the result file attached to the fine-tuning job for your model and analyze your custom model for training and validation performance.
Select Refresh to update the information on the page.
Checkpoints
When each training epoch completes a checkpoint is generated. A checkpoint is a fully functional version of a model which can both be deployed and used as the target model for subsequent fine-tuning jobs. Checkpoints can be particularly useful, as they can provide a snapshot of your model prior to overfitting having occurred. When a fine-tuning job completes you will have the three most recent versions of the model available to deploy.
Safety evaluation GPT-4 fine-tuning - public preview
GPT-4 and GPT-4o-mini are our most advanced models that can be fine-tuned to your needs. As with Azure OpenAI models generally, the advanced capabilities of fine-tuned models come with increased responsible AI challenges related to harmful content, manipulation, human-like behavior, privacy issues, and more. Learn more about risks, capabilities, and limitations in the Overview of Responsible AI practices and Transparency Note. To help mitigate the risks associated with GPT-4 and GPT-4o-mini fine-tuned models, we have implemented additional evaluation steps to help detect and prevent harmful content in the training and outputs of fine-tuned models. These steps are grounded in the Microsoft Responsible AI Standard and Azure OpenAI Service content filtering.
Evaluations are conducted in dedicated, customer specific, private workspaces;
Evaluation endpoints are in the same geography as the Azure OpenAI resource;
Training data is not stored in connection with performing evaluations; only the final model assessment (deployable or not deployable) is persisted; and
GPT-4 and GPT-4o-mini fine-tuned model evaluation filters are set to predefined thresholds and cannot be modified by customers; they aren't tied to any custom content filtering configuration you may have created.
Data evaluation
Before training starts, your data is evaluated for potentially harmful content (violence, sexual, hate, and fairness, self-harm – see category definitions here). If harmful content is detected above the specified severity level, your training job will fail, and you'll receive a message informing you of the categories of failure.
Sample message:
Output
The provided training data failed RAI checks for harm types: [hate_fairness, self_harm, violence]. Please fix the data and try again.
Your training data is evaluated automatically within your data import job as part of providing the fine-tuning capability.
If the fine-tuning job fails due to the detection of harmful content in training data, you won't be charged.
Model evaluation
After training completes but before the fine-tuned model is available for deployment, the resulting model is evaluated for potentially harmful responses using Azure’s built-in risk and safety metrics. Using the same approach to testing that we use for the base large language models, our evaluation capability simulates a conversation with your fine-tuned model to assess the potential to output harmful content, again using specified harmful content categories (violence, sexual, hate, and fairness, self-harm).
If a model is found to generate output containing content detected as harmful at above an acceptable rate, you'll be informed that your model isn't available for deployment, with information about the specific categories of harm detected:
Sample Message:
Output
This model is unable to be deployed. Model evaluation identified that this fine tuned model scores above acceptable thresholds for
[Violence, Self Harm]. Please review your training data set and resubmit the job.
As with data evaluation, the model is evaluated automatically within your fine-tuning job as part of providing the fine-tuning capability. Only the resulting assessment (deployable or not deployable) is logged by the service. If deployment of the fine-tuned model fails due to the detection of harmful content in model outputs, you won't be charged for the training run.
Deploy a fine-tuned model
When the fine-tuning job succeeds, you can deploy the custom model from the Models pane. You must deploy your custom model to make it available for use with completion calls.
Important
After you deploy a customized model, if at any time the deployment remains inactive for greater than fifteen (15) days, the deployment is deleted. The deployment of a customized model is inactive if the model was deployed more than fifteen (15) days ago and no completions or chat completions calls were made to it during a continuous 15-day period.
The deletion of an inactive deployment doesn't delete or affect the underlying customized model, and the customized model can be redeployed at any time. As described in Azure OpenAI Service pricing, each customized (fine-tuned) model that's deployed incurs an hourly hosting cost regardless of whether completions or chat completions calls are being made to the model. To learn more about planning and managing costs with Azure OpenAI, refer to the guidance in Plan to manage costs for Azure OpenAI Service.
Note
Only one deployment is permitted for a custom model. An error message is displayed if you select an already-deployed custom model.
To deploy your custom model, select the custom model to deploy, and then select Deploy model.
The Deploy model dialog box opens. In the dialog box, enter your Deployment name and then select Create to start the deployment of your custom model.
You can monitor the progress of your deployment on the Deployments pane in Azure OpenAI Studio.
Cross region deployment
Fine-tuning supports deploying a fine-tuned model to a different region than where the model was originally fine-tuned. You can also deploy to a different subscription/region.
The only limitations are that the new region must also support fine-tuning and when deploying cross subscription the account generating the authorization token for the deployment must have access to both the source and destination subscriptions.
Cross subscription/region deployment can be accomplished via Python or REST.
Use a deployed custom model
After your custom model deploys, you can use it like any other deployed model. You can use the Playgrounds in Azure OpenAI Studio to experiment with your new deployment. You can continue to use the same parameters with your custom model, such as temperature and max_tokens, as you can with other deployed models. For fine-tuned babbage-002 and davinci-002 models you will use the Completions playground and the Completions API. For fine-tuned gpt-35-turbo-0613 models you will use the Chat playground and the Chat completion API.
Analyze your custom model
Azure OpenAI attaches a result file named results.csv to each fine-tuning job after it completes. You can use the result file to analyze the training and validation performance of your custom model. The file ID for the result file is listed for each custom model in the Result file Id column on the Models pane for Azure OpenAI Studio. You can use the file ID to identify and download the result file from the Data files pane of Azure OpenAI Studio.
The result file is a CSV file that contains a header row and a row for each training step performed by the fine-tuning job. The result file contains the following columns:
Column name
Description
step
The number of the training step. A training step represents a single pass, forward and backward, on a batch of training data.
train_loss
The loss for the training batch.
train_mean_token_accuracy
The percentage of tokens in the training batch correctly predicted by the model. For example, if the batch size is set to 3 and your data contains completions [[1, 2], [0, 5], [4, 2]], this value is set to 0.83 (5 of 6) if the model predicted [[1, 1], [0, 5], [4, 2]].
valid_loss
The loss for the validation batch.
validation_mean_token_accuracy
The percentage of tokens in the validation batch correctly predicted by the model. For example, if the batch size is set to 3 and your data contains completions [[1, 2], [0, 5], [4, 2]], this value is set to 0.83 (5 of 6) if the model predicted [[1, 1], [0, 5], [4, 2]].
full_valid_loss
The validation loss calculated at the end of each epoch. When training goes well, loss should decrease.
full_valid_mean_token_accuracy
The valid mean token accuracy calculated at the end of each epoch. When training is going well, token accuracy should increase.
You can also view the data in your results.csv file as plots in Azure OpenAI Studio. Select the link for your trained model, and you will see three charts: loss, mean token accuracy, and token accuracy. If you provided validation data, both datasets will appear on the same plot.
Look for your loss to decrease over time, and your accuracy to increase. If you see a divergence between your training and validation data, that may indicate that you are overfitting. Try training with fewer epochs, or a smaller learning rate multiplier.
Clean up your deployments, custom models, and training files
When you're done with your custom model, you can delete the deployment and model. You can also delete the training and validation files you uploaded to the service, if needed.
Delete your model deployment
Important
After you deploy a customized model, if at any time the deployment remains inactive for greater than fifteen (15) days, the deployment is deleted. The deployment of a customized model is inactive if the model was deployed more than fifteen (15) days ago and no completions or chat completions calls were made to it during a continuous 15-day period.
The deletion of an inactive deployment doesn't delete or affect the underlying customized model, and the customized model can be redeployed at any time. As described in Azure OpenAI Service pricing, each customized (fine-tuned) model that's deployed incurs an hourly hosting cost regardless of whether completions or chat completions calls are being made to the model. To learn more about planning and managing costs with Azure OpenAI, refer to the guidance in Plan to manage costs for Azure OpenAI Service.
You can delete the deployment for your custom model on the Deployments pane in Azure OpenAI Studio. Select the deployment to delete, and then select Delete to delete the deployment.
Delete your custom model
You can delete a custom model on the Models pane in Azure OpenAI Studio. Select the custom model to delete from the Customized models tab, and then select Delete to delete the custom model.
Note
You can't delete a custom model if it has an existing deployment. You must first delete your model deployment before you can delete your custom model.
Delete your training files
You can optionally delete training and validation files that you uploaded for training, and result files generated during training, on the Management > Data files pane in Azure OpenAI Studio. Select the file to delete, and then select Delete to delete the file.
Continuous fine-tuning
Once you have created a fine-tuned model you may wish to continue to refine the model over time through further fine-tuning. Continuous fine-tuning is the iterative process of selecting an already fine-tuned model as a base model and fine-tuning it further on new sets of training examples.
To perform fine-tuning on a model that you have previously fine-tuned you would use the same process as described in create a customized model but instead of specifying the name of a generic base model you would specify your already fine-tuned model. A custom fine-tuned model would look like gpt-35-turbo-0613.ft-5fd1918ee65d4cd38a5dcf6835066ed7
We also recommend including the suffix parameter to make it easier to distinguish between different iterations of your fine-tuned model. suffix takes a string, and is set to identify the fine-tuned model. With the OpenAI Python API a string of up to 18 characters is supported that will be added to your fine-tuned model name.
Troubleshooting
How do I enable fine-tuning? Create a custom model is greyed out in Azure OpenAI Studio?
In order to successfully access fine-tuning, you need Cognitive Services OpenAI Contributor assigned. Even someone with high-level Service Administrator permissions would still need this account explicitly set in order to access fine-tuning. For more information, please review the role-based access control guidance.
Why did my upload fail?
If your file upload fails in Azure OpenAI Studio, you can view the error message under “data files” in Azure OpenAI Studio. Hover your mouse over where it says “error” (under the status column) and an explanation of the failure will be displayed.
My fine-tuned model does not seem to have improved
Missing system message: You need to provide a system message when you fine tune; you will want to provide that same system message when you use the fine-tuned model. If you provide a different system message, you may see different results than what you fine-tuned for.
Not enough data: while 10 is the minimum for the pipeline to run, you need hundreds to thousands of data points to teach the model a new skill. Too few data points risks overfitting and poor generalization. Your fine-tuned model may perform well on the training data, but poorly on other data because it has memorized the training examples instead of learning patterns. For best results, plan to prepare a data set with hundreds or thousands of data points.
Bad data: A poorly curated or unrepresentative dataset will produce a low-quality model. Your model may learn inaccurate or biased patterns from your dataset. For example, if you are training a chatbot for customer service, but only provide training data for one scenario (e.g. item returns) it will not know how to respond to other scenarios. Or, if your training data is bad (contains incorrect responses), your model will learn to provide incorrect results.
