Merge pull request #260032 from mrbullwinkle/mrb_12_01_2023_fine_tuning

[Azure OpenAI] fine-tuning 1.x updates

Author: prmerger-automator[bot]

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

@@ -140,6 +140,8 @@ For large data files, we recommend that you import from an Azure Blob  store. La
 
 The following Python example uploads local training and validation files by using the Python SDK, and retrieves the returned file IDs.
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 # Upload fine-tuning files
 
@@ -170,12 +172,50 @@ print("Training file ID:", training_file_id)
 print("Validation file ID:", validation_file_id)
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+# Upload fine-tuning files
+
+import os
+from openai import AzureOpenAI
+
+client = AzureOpenAI(
+  azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT"), 
+  api_key=os.getenv("AZURE_OPENAI_KEY"),  
+  api_version="2023-10-01-preview"  # This API version or later is required to access fine-tuning for turbo/babbage-002/davinci-002
+)
+
+training_file_name = 'training_set.jsonl'
+validation_file_name = 'validation_set.jsonl'
+
+# Upload the training and validation dataset files to Azure OpenAI with the SDK.
+
+training_response = client.files.create(
+    file=open(training_file_name, "rb"), purpose="fine-tune"
+)
+training_file_id = training_response.id
+
+validation_response = client.files.create(
+    file=open(validation_file_name, "rb"), purpose="fine-tune"
+)
+validation_file_id = validation_response.id
+
+print("Training file ID:", training_file_id)
+print("Validation file ID:", validation_file_id)
+
+```
+
+---
+
 ## Create a customized model
 
 After you upload your training and validation files, you're ready to start the fine-tuning job.
 
 The following Python code shows an example of how to create a new fine-tune job with the Python SDK:
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 
 response = openai.FineTuningJob.create(
@@ -195,8 +235,31 @@ print(response)
 
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+response = client.fine_tuning.jobs.create(
+    training_file=training_file_id,
+    validation_file=validation_file_id,
+    model="gpt-35-turbo", # Enter base model name. Note that in Azure OpenAI the model name contains dashes and cannot contain dot/period characters. 
+)
+
+job_id = response.id
+
+# You can use the job ID to monitor the status of the fine-tuning job.
+# The fine-tuning job will take some time to start and complete.
+
+print("Job ID:", response.id)
+print("Status:", response.id)
+print(response.model_dump_json(indent=2))
+```
+
+---
+
 ## Check fine-tuning job status
 
+# [OpenAI Python 0.28.1](#tab/python)
+
 ```python
 #Retrieve training job ID
 
@@ -207,6 +270,18 @@ print("Status:", response["status"])
 print(response)
 ```
 
+# [OpenAI Python 1.x](#tab/python-new)
+
+```python
+response = client.fine_tuning.jobs.retrieve(job_id)
+
+print("Job ID:", response.id)
+print("Status:", response.status)
+print(response.model_dump_json(indent=2))
+```
+
+---
+
 ## Deploy a customized model
 
 When the fine-tune job succeeds, the value of the `fine_tuned_model` variable in the response body is set to the name of your customized model. Your model is now also available for discovery from the [list Models API](/rest/api/azureopenai/models/list). However, you can't issue completion calls to your customized model until your customized model is deployed. You must deploy your customized model to make it available for use with completion calls.