feat(fine-tuning): fix Azure OpenAI fine-tuning job creation

- Default trainingType=1 for Azure when omitted to avoid misleading "base model does not support fine-tuning" error
- Normalize Azure FineTuningJob responses (pending→queued, null fields→defaults) to match OpenAI schema
- Add pending status support to OpenAIFileObject for Azure file uploads
- Add test coverage for trainingType default and response normalization

Made-with: Cursor

litellm/fine_tuning/main.py

@@ -245,6 +245,15 @@ def create_fine_tuning_job(
             )
         # Azure OpenAI
         elif custom_llm_provider == "azure":
+            # Azure requires trainingType (e.g. 1 = supervised). Omitting it yields a misleading
+            # "The specified base model does not support fine-tuning" error from the service.
+            if kwargs.get("trainingType") is None:
+                _eb = (
+                    kwargs.get("extra_body") or optional_params.get("extra_body") or {}
+                )
+                if not (isinstance(_eb, dict) and _eb.get("trainingType") is not None):
+                    kwargs["trainingType"] = 1
+
             api_base = optional_params.api_base or litellm.api_base or get_secret_str("AZURE_API_BASE")  # type: ignore
 
             api_version = (

litellm/llms/openai/fine_tuning/handler.py

@@ -1,4 +1,4 @@
-from typing import Any, Coroutine, Optional, Union, cast
+from typing import Any, Coroutine, Dict, Optional, Union, cast
 
 import httpx
 from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
@@ -7,6 +7,35 @@ from litellm._logging import verbose_logger
 from litellm.types.utils import LiteLLMFineTuningJob
 
 
+def _normalize_fine_tuning_job_dict(data: Dict[str, Any]) -> Dict[str, Any]:
+    """
+    Normalize Azure OpenAI FineTuningJob response to match OpenAI schema.
+
+    Azure differences:
+    - organization_id: null → ""
+    - result_files: null → []
+    - status: "pending" → "queued"
+    """
+    normalized = data.copy()
+
+    if normalized.get("organization_id") is None:
+        normalized["organization_id"] = ""
+
+    if normalized.get("result_files") is None:
+        normalized["result_files"] = []
+
+    if normalized.get("status") == "pending":
+        normalized["status"] = "queued"
+
+    return normalized
+
+
+def _litellm_fine_tuning_job_from_response(response: Any) -> LiteLLMFineTuningJob:
+    return LiteLLMFineTuningJob(
+        **_normalize_fine_tuning_job_dict(response.model_dump())
+    )
+
+
 class OpenAIFineTuningAPI:
     """
     OpenAI methods to support for batches
@@ -60,7 +89,7 @@ class OpenAIFineTuningAPI:
             **create_fine_tuning_job_data
         )
 
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
 
     def create_fine_tuning_job(
         self,
@@ -108,7 +137,7 @@ class OpenAIFineTuningAPI:
         response = cast(OpenAI, openai_client).fine_tuning.jobs.create(
             **create_fine_tuning_job_data
         )
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
 
     async def acancel_fine_tuning_job(
         self,
@@ -118,7 +147,7 @@ class OpenAIFineTuningAPI:
         response = await openai_client.fine_tuning.jobs.cancel(
             fine_tuning_job_id=fine_tuning_job_id
         )
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
 
     def cancel_fine_tuning_job(
         self,
@@ -164,7 +193,7 @@ class OpenAIFineTuningAPI:
         response = cast(OpenAI, openai_client).fine_tuning.jobs.cancel(
             fine_tuning_job_id=fine_tuning_job_id
         )
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
 
     async def alist_fine_tuning_jobs(
         self,
@@ -229,7 +258,7 @@ class OpenAIFineTuningAPI:
         response = await openai_client.fine_tuning.jobs.retrieve(
             fine_tuning_job_id=fine_tuning_job_id
         )
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)
 
     def retrieve_fine_tuning_job(
         self,
@@ -275,4 +304,4 @@ class OpenAIFineTuningAPI:
         response = cast(OpenAI, openai_client).fine_tuning.jobs.retrieve(
             fine_tuning_job_id=fine_tuning_job_id
         )
-        return LiteLLMFineTuningJob(**response.model_dump())
+        return _litellm_fine_tuning_job_from_response(response)

tests/batches_tests/test_fine_tuning_api.py

@@ -180,6 +180,34 @@ async def test_azure_create_fine_tune_jobs_async():
     pass
 
 
+def test_azure_trainingtype_defaults_to_one():
+    """
+    Azure requires trainingType in extra_body. When omitted, LiteLLM defaults it to 1.
+    """
+    from unittest.mock import MagicMock, patch
+    from litellm.fine_tuning.main import create_fine_tuning_job
+
+    with patch(
+        "litellm.fine_tuning.main.azure_fine_tuning_apis_instance.create_fine_tuning_job"
+    ) as mock_create:
+        mock_create.return_value = MagicMock(
+            id="ftjob-test", status="queued", model="gpt-4o-mini"
+        )
+
+        create_fine_tuning_job(
+            model="gpt-4o-mini",
+            training_file="file-test",
+            custom_llm_provider="azure",
+            api_base="https://test.openai.azure.com",
+            api_key="test-key",
+        )
+
+        call_kwargs = mock_create.call_args[1]
+        create_data = call_kwargs["create_fine_tuning_job_data"]
+        assert "extra_body" in create_data
+        assert create_data["extra_body"].get("trainingType") == 1
+
+
 @pytest.mark.asyncio()
 async def test_create_vertex_fine_tune_jobs_mocked():
     load_vertex_ai_credentials()

tests/test_litellm/types/llms/test_types_llms_openai.py

@@ -219,11 +219,13 @@ class TestAssistantMessageImageUrlContent:
         # convert to list to consume it — this must not raise ValidationError.
         content_blocks = list(raw_content) if raw_content is not None else []
 
-        assert len(content_blocks) == 2, (
-            f"Expected 2 content blocks (text + image_url), got {len(content_blocks)}: {content_blocks}"
-        )
+        assert (
+            len(content_blocks) == 2
+        ), f"Expected 2 content blocks (text + image_url), got {len(content_blocks)}: {content_blocks}"
         types = [b.get("type") for b in content_blocks if isinstance(b, dict)]
-        assert "image_url" in types, f"image_url block was silently dropped; blocks: {content_blocks}"
+        assert (
+            "image_url" in types
+        ), f"image_url block was silently dropped; blocks: {content_blocks}"
 
     def test_assistant_message_image_url_preserved_in_all_message_values(self):
         """
@@ -255,14 +257,16 @@ class TestAssistantMessageImageUrlContent:
         assert assistant is not None, "Assistant message missing after serialisation"
 
         content = assistant.get("content", [])
-        assert isinstance(content, list), f"content should be a list, got {type(content)}"
-        assert len(content) == 2, (
-            f"Expected 2 content blocks (text + image_url), got {len(content)}: {content}"
-        )
+        assert isinstance(
+            content, list
+        ), f"content should be a list, got {type(content)}"
+        assert (
+            len(content) == 2
+        ), f"Expected 2 content blocks (text + image_url), got {len(content)}: {content}"
         types = [b.get("type") for b in content if isinstance(b, dict)]
-        assert "image_url" in types, (
-            f"image_url block was silently dropped during AllMessageValues serialisation; blocks: {content}"
-        )
+        assert (
+            "image_url" in types
+        ), f"image_url block was silently dropped during AllMessageValues serialisation; blocks: {content}"
 
 
 class TestResponsesAPIReasoningNullFields:
@@ -379,10 +383,14 @@ class TestResponsesAPIReasoningNullFields:
         )
         dumped = response.model_dump()
         reasoning = [
-            o for o in dumped["output"] if isinstance(o, dict) and o.get("type") == "reasoning"
+            o
+            for o in dumped["output"]
+            if isinstance(o, dict) and o.get("type") == "reasoning"
         ][0]
         message = [
-            o for o in dumped["output"] if isinstance(o, dict) and o.get("type") == "message"
+            o
+            for o in dumped["output"]
+            if isinstance(o, dict) and o.get("type") == "message"
         ][0]
         assert "status" not in reasoning
         assert "content" not in reasoning
@@ -410,3 +418,14 @@ class TestResponsesAPIReasoningNullFields:
         assert dumped["error"] is None
         assert "instructions" in dumped
         assert dumped["instructions"] is None
+
+
+def test_normalize_fine_tuning_job_dict_maps_azure_pending():
+    from litellm.llms.openai.fine_tuning.handler import _normalize_fine_tuning_job_dict
+
+    out = _normalize_fine_tuning_job_dict(
+        {"organization_id": None, "result_files": None, "status": "pending"}
+    )
+    assert out["organization_id"] == ""
+    assert out["result_files"] == []
+    assert out["status"] == "queued"