Merge pull request #3548 from voutilad/ft-remove-gpt4

Remove turbo-0613 and gpt4-0613.

Author: prmerger-automator[bot]

articles/ai-services/openai/how-to/fine-tuning-deploy.md

@@ -56,7 +56,7 @@ deploy_data = {
     "properties": {
         "model": {
             "format": "OpenAI",
-            "name": <"fine_tuned_model">, #retrieve this value from the previous call, it will look like gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83
+            "name": <"fine_tuned_model">, #retrieve this value from the previous call, it will look like gpt-35-turbo-0125.ft-b044a9d3cf9c4228b5d393567f693b83
             "version": "1"
         }
     }
@@ -82,7 +82,7 @@ print(r.json())
 | resource_group | The resource group name for your Azure OpenAI resource. |
 | resource_name | The Azure OpenAI resource name. |
 | model_deployment_name | The custom name for your new fine-tuned model deployment. This is the name that will be referenced in your code when making chat completion calls. |
-| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83`. You will need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
+| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0125.ft-b044a9d3cf9c4228b5d393567f693b83`. You will need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
 
 ### Cross region deployment
 
@@ -122,7 +122,7 @@ deploy_data = {
     "properties": {
         "model": {
             "format": "OpenAI",
-            "name": <"FINE_TUNED_MODEL_NAME">, # This value will look like gpt-35-turbo-0613.ft-0ab3f80e4f2242929258fff45b56a9ce 
+            "name": <"FINE_TUNED_MODEL_NAME">, # This value will look like gpt-35-turbo-0125.ft-0ab3f80e4f2242929258fff45b56a9ce 
             "version": "1",
             "source": source
         }
@@ -220,7 +220,7 @@ curl -X POST "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resource
 | resource_group | The resource group name for your Azure OpenAI resource. |
 | resource_name | The Azure OpenAI resource name. |
 | model_deployment_name | The custom name for your new fine-tuned model deployment. This is the name that will be referenced in your code when making chat completion calls. |
-| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83`. You'll need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
+| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0125.ft-b044a9d3cf9c4228b5d393567f693b83`. You'll need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
 
 
 ### Cross region deployment

articles/ai-services/openai/includes/fine-tune-models.md

@@ -17,11 +17,7 @@ manager: nitinme
 
 |  Model ID  | Fine-tuning regions | Max request (tokens) | Training Data (up to) |
 |  --- | --- | :---: | :---: |
-| `gpt-35-turbo` (0613) | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | 4,096 | Sep 2021 |
 | `gpt-35-turbo` (1106) | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | Input: 16,385<br> Output: 4,096 |  Sep 2021|
 | `gpt-35-turbo` (0125)  | East US2 <br> North Central US <br> Sweden Central <br> Switzerland West | 16,385 | Sep 2021 |
-| `gpt-4` (0613) <sup>**1**</sup> | North Central US <br> Sweden Central | 8192 | Sep 2021 |
 | `gpt-4o-mini` (2024-07-18) | North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 64,536 | Oct 2023 |
 | `gpt-4o` (2024-08-06) | East US2 <br> North Central US <br> Sweden Central | Input: 128,000 <br> Output: 16,384  <br> Training example context length: 64,536 | Oct 2023 | 
-
-**<sup>1</sup>** GPT-4 is currently in public preview.

articles/ai-services/openai/includes/fine-tuning-openai-in-ai-studio.md

@@ -28,15 +28,11 @@ ms.custom: include, build-2024
 
 The following models support fine-tuning:
 
-- `gpt-35-turbo` (0613)
 - `gpt-35-turbo` (1106)
 - `gpt-35-turbo` (0125)
-- `gpt-4` (0613)**<sup>*</sup>**
 - `gpt-4o` (2024-08-06)
 - `gpt-4o-mini` (2024-07-18)
 
-**<sup>*</sup>** Fine-tuning for this model is currently in public preview.
-
 Or you can fine tune a previously fine-tuned model, formatted as base-model.ft-{jobid}.
 
 Consult the [models page](../concepts/models.md#fine-tuning-models) to check which regions currently support fine-tuning.
@@ -62,7 +58,7 @@ Take a moment to review the fine-tuning workflow for using Azure AI Foundry:
 
 Your training data and validation data sets consist of input and output examples for how you would like the model to perform.
 
-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](../how-to/chatgpt.md) API.
+The training and validation data you use **must** be formatted as a JSON Lines (JSONL) document and must be formatted in the conversational format that is used by the [Chat completions](../how-to/chatgpt.md) API.
 
 It's generally recommended to use the instructions and prompts that you found worked best in every training example. This will help you get the best results, especially if you have fewer than a hundred examples.
 
@@ -117,7 +113,7 @@ To fine-tune an Azure OpenAI model in an existing Azure AI Foundry project, foll
 
 1. Select a base model to fine-tune. Your choice influences both the performance and the cost of your model. In this example, we are choosing the `gpt-35-turbo` model. Then select **Confirm**.
 
-1. For `gpt-35-turbo` we have different versions available for fine-tuning, so please choose which version you'd like to fine-tune. We will choose (0301). 
+1. For `gpt-35-turbo` we have different versions available for fine-tuning, so please choose which version you'd like to fine-tune. We will choose (0125). 
 
 1. 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.
 

articles/ai-services/openai/includes/fine-tuning-python.md

@@ -26,15 +26,11 @@ ms.author: mbullwin
 
 The following models support fine-tuning:
 
-- `gpt-35-turbo` (0613)
 - `gpt-35-turbo` (1106)
 - `gpt-35-turbo` (0125)
-- `gpt-4` (0613)**<sup>*</sup>**
 - `gpt-4o` (2024-08-06)
 - `gpt-4o-mini` (2024-07-18)
 
-**<sup>*</sup>** Fine-tuning for this model is currently in public preview.
-
 Or you can fine tune a previously fine-tuned model, formatted as `base-model.ft-{jobid}`.
 
 
@@ -57,9 +53,9 @@ Take a moment to review the fine-tuning workflow for using the Python SDK with A
 
 Your training data and validation data sets consist of input and output examples for how you would like the model to perform.
 
-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](../how-to/chatgpt.md) API.
+The training and validation data you use **must** be formatted as a JSON Lines (JSONL) document and must be formatted in the conversational format that is used by the [Chat completions](../how-to/chatgpt.md) API.
 
-If you would like a step-by-step walk-through of fine-tuning a `gpt-35-turbo-0613` please refer to the [Azure OpenAI fine-tuning tutorial](../tutorials/fine-tune.md)
+If you would like a step-by-step walk-through of fine-tuning a `gpt-4o-mini-2024-07-18` please refer to the [Azure OpenAI fine-tuning tutorial](../tutorials/fine-tune.md)
 
 ### Example file format
 
@@ -196,7 +192,7 @@ In this example we are also passing the seed parameter. The seed controls the re
 response = client.fine_tuning.jobs.create(
     training_file=training_file_id,
     validation_file=validation_file_id,
-    model="gpt-35-turbo-0613", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
+    model="gpt-35-turbo-0125", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
     seed = 105  # seed parameter controls reproducibility of the fine-tuning job. If no seed is specified one will be generated automatically.
 )
 
@@ -235,7 +231,7 @@ client = AzureOpenAI(
 
 client.fine_tuning.jobs.create(
   training_file="file-abc123", 
-  model="gpt-35-turbo-0613", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
+  model="gpt-35-turbo-0125", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
   hyperparameters={
     "n_epochs":2
   }
@@ -327,7 +323,7 @@ Unlike the previous SDK commands, deployment must be done using the control plan
 | resource_group | The resource group name for your Azure OpenAI resource. |
 | resource_name | The Azure OpenAI resource name. |
 | model_deployment_name | The custom name for your new fine-tuned model deployment. This is the name that will be referenced in your code when making chat completion calls. |
-| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83`. You will need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
+| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0125.ft-b044a9d3cf9c4228b5d393567f693b83`. You will need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
 
 ```python
 import json
@@ -348,7 +344,7 @@ deploy_data = {
     "properties": {
         "model": {
             "format": "OpenAI",
-            "name": <"fine_tuned_model">, #retrieve this value from the previous call, it will look like gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83
+            "name": <"fine_tuned_model">, #retrieve this value from the previous call, it will look like gpt-35-turbo-0125.ft-b044a9d3cf9c4228b5d393567f693b83
             "version": "1"
         }
     }
@@ -374,7 +370,7 @@ Learn more about cross region deployment and use the deployed model [here](../ho
 
 Once you have created a fine-tuned model you might want 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](#create-a-customized-model) but instead of specifying the name of a generic base model you would specify your already fine-tuned model's ID. The fine-tuned model ID looks like `gpt-35-turbo-0613.ft-5fd1918ee65d4cd38a5dcf6835066ed7`
+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](#create-a-customized-model) but instead of specifying the name of a generic base model you would specify your already fine-tuned model's ID. The fine-tuned model ID looks like `gpt-35-turbo-0125.ft-5fd1918ee65d4cd38a5dcf6835066ed7`
 
 ```python
 from openai import AzureOpenAI
@@ -388,7 +384,7 @@ client = AzureOpenAI(
 response = client.fine_tuning.jobs.create(
     training_file=training_file_id,
     validation_file=validation_file_id,
-    model="gpt-35-turbo-0613.ft-5fd1918ee65d4cd38a5dcf6835066ed7" # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
+    model="gpt-35-turbo-0125.ft-5fd1918ee65d4cd38a5dcf6835066ed7" # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
 )
 
 job_id = response.id

articles/ai-services/openai/includes/fine-tuning-rest.md

@@ -25,15 +25,11 @@ ms.author: mbullwin
 
 The following models support fine-tuning:
 
-- `gpt-35-turbo` (0613)
 - `gpt-35-turbo` (1106)
 - `gpt-35-turbo` (0125)
-- `gpt-4` (0613)**<sup>*</sup>**
 - `gpt-4o` (2024-08-06)
 - `gpt-4o-mini` (2024-07-18)
 
-**<sup>*</sup>** Fine-tuning for this model is currently in public preview.
-
 Or you can fine tune a previously fine-tuned model, formatted as base-model.ft-{jobid}.
 
 Consult the [models page](../concepts/models.md#fine-tuning-models) to check which regions currently support fine-tuning.
@@ -57,9 +53,9 @@ Take a moment to review the fine-tuning workflow for using the REST APIS and Pyt
 
 Your training data and validation data sets consist of input and output examples for how you would like the model to perform.
 
-The training and validation data you use **must** be formatted as a JSON Lines (JSONL) document. For `gpt-35-turbo-0613` and other related models, the fine-tuning dataset must be formatted in the conversational format that is used by the [Chat completions](../how-to/chatgpt.md) API.
+The training and validation data you use **must** be formatted as a JSON Lines (JSONL) document and must be formatted in the conversational format that is used by the [Chat completions](../how-to/chatgpt.md) API.
 
-If you would like a step-by-step walk-through of fine-tuning a `gpt-35-turbo-0613` please refer to the [Azure OpenAI fine-tuning tutorial.](../tutorials/fine-tune.md)
+If you would like a step-by-step walk-through of fine-tuning a `gpt-4o-mini-2024-07-18` please refer to the [Azure OpenAI fine-tuning tutorial.](../tutorials/fine-tune.md)
 
 ### Example file format
 
@@ -141,7 +137,7 @@ curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs?api-version=2024-10-
   -H "Content-Type: application/json" \
   -H "api-key: $AZURE_OPENAI_API_KEY" \
   -d '{
-    "model": "gpt-35-turbo-0613", 
+    "model": "gpt-35-turbo-0125", 
     "training_file": "<TRAINING_FILE_ID>", 
     "validation_file": "<VALIDATION_FILE_ID>",
     "seed": 105
@@ -237,7 +233,7 @@ The following example shows how to use the REST API to create a model deployment
 | resource_group | The resource group name for your Azure OpenAI resource. |
 | resource_name | The Azure OpenAI resource name. |
 | model_deployment_name | The custom name for your new fine-tuned model deployment. This is the name that will be referenced in your code when making chat completion calls. |
-| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0613.ft-b044a9d3cf9c4228b5d393567f693b83`. You'll need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
+| fine_tuned_model | Retrieve this value from your fine-tuning job results in the previous step. It will look like `gpt-35-turbo-0125.ft-b044a9d3cf9c4228b5d393567f693b83`. You'll need to add that value to the deploy_data json. Alternatively you can also deploy a checkpoint, by passing the checkpoint ID which will appear in the format `ftchkpt-e559c011ecc04fc68eaa339d8227d02d` |
 
 ```bash
 curl -X POST "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceGroups/<RESOURCE_GROUP>/providers/Microsoft.CognitiveServices/accounts/<RESOURCE_NAME>/deployments/<MODEL_DEPLOYMENT_NAME>api-version=2024-10-21" \
@@ -262,14 +258,14 @@ Learn more about cross region deployment and use the deployed model [here](../ho
 
 Once you have created a fine-tuned model, you might want 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](#create-a-customized-model) but instead of specifying the name of a generic base model you would specify your already fine-tuned model's ID. The fine-tuned model ID looks like `gpt-35-turbo-0613.ft-5fd1918ee65d4cd38a5dcf6835066ed7`
+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](#create-a-customized-model) but instead of specifying the name of a generic base model you would specify your already fine-tuned model's ID. The fine-tuned model ID looks like `gpt-35-turbo-0125.ft-5fd1918ee65d4cd38a5dcf6835066ed7`
 
 ```bash
 curl -X POST $AZURE_OPENAI_ENDPOINT/openai/fine_tuning/jobs?api-version=2023-12-01-preview \
   -H "Content-Type: application/json" \
   -H "api-key: $AZURE_OPENAI_API_KEY" \
   -d '{
-    "model": "gpt-35-turbo-0613.ft-5fd1918ee65d4cd38a5dcf6835066ed7", 
+    "model": "gpt-35-turbo-0125.ft-5fd1918ee65d4cd38a5dcf6835066ed7", 
     "training_file": "<TRAINING_FILE_ID>", 
     "validation_file": "<VALIDATION_FILE_ID>",
     "suffix": "<additional text used to help identify fine-tuned models>"