From 265f2eb09030a6bde25c86e94860530604c18167 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Mon, 23 Mar 2026 12:11:04 +0530
Subject: [PATCH 1/6] feat(fine-tuning): fix Azure OpenAI fine-tuning job
 creation
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 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                   |  9 ++++
 litellm/llms/openai/fine_tuning/handler.py    | 43 +++++++++++++++---
 tests/batches_tests/test_fine_tuning_api.py   | 28 ++++++++++++
 .../types/llms/test_types_llms_openai.py      | 45 +++++++++++++------
 4 files changed, 105 insertions(+), 20 deletions(-)

diff --git a/litellm/fine_tuning/main.py b/litellm/fine_tuning/main.py
index 08373cda782..a611488b179 100644
--- a/litellm/fine_tuning/main.py
+++ b/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 = (
diff --git a/litellm/llms/openai/fine_tuning/handler.py b/litellm/llms/openai/fine_tuning/handler.py
index 9804ff3539e..581ca1eea8c 100644
--- a/litellm/llms/openai/fine_tuning/handler.py
+++ b/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)
diff --git a/tests/batches_tests/test_fine_tuning_api.py b/tests/batches_tests/test_fine_tuning_api.py
index 7e238173480..b7ff8c7fae7 100644
--- a/tests/batches_tests/test_fine_tuning_api.py
+++ b/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()
diff --git a/tests/test_litellm/types/llms/test_types_llms_openai.py b/tests/test_litellm/types/llms/test_types_llms_openai.py
index 94221bd0efc..384d0dd73d7 100644
--- a/tests/test_litellm/types/llms/test_types_llms_openai.py
+++ b/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"

From 2484d202f827e838b7d2550ae98d92fc5ef9f402 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Mon, 23 Mar 2026 12:26:14 +0530
Subject: [PATCH 2/6] address greptile review feedback (greploop iteration 1)

- Move trainingType injection to AzureOpenAIFineTuningAPI handler
- Guard normalization with is_azure flag to only apply to Azure responses
- Override acreate_fine_tuning_job in Azure handler to use is_azure=True
- Update test to directly test _ensure_training_type method
- Add test for OpenAI unchanged behavior

Made-with: Cursor
---
 litellm/fine_tuning/main.py                   |  9 --
 litellm/llms/azure/fine_tuning/handler.py     | 85 ++++++++++++++++++-
 litellm/llms/openai/fine_tuning/handler.py    | 13 ++-
 tests/batches_tests/test_fine_tuning_api.py   | 27 ++----
 .../types/llms/test_types_llms_openai.py      | 11 ++-
 5 files changed, 110 insertions(+), 35 deletions(-)

diff --git a/litellm/fine_tuning/main.py b/litellm/fine_tuning/main.py
index a611488b179..08373cda782 100644
--- a/litellm/fine_tuning/main.py
+++ b/litellm/fine_tuning/main.py
@@ -245,15 +245,6 @@ 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 = (
diff --git a/litellm/llms/azure/fine_tuning/handler.py b/litellm/llms/azure/fine_tuning/handler.py
index 429b8349896..2415c7d78ed 100644
--- a/litellm/llms/azure/fine_tuning/handler.py
+++ b/litellm/llms/azure/fine_tuning/handler.py
@@ -1,10 +1,15 @@
-from typing import Optional, Union
+from typing import Any, Coroutine, Dict, Optional, Union, cast
 
 import httpx
 from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
 
+from litellm._logging import verbose_logger
 from litellm.llms.azure.common_utils import BaseAzureLLM
-from litellm.llms.openai.fine_tuning.handler import OpenAIFineTuningAPI
+from litellm.llms.openai.fine_tuning.handler import (
+    OpenAIFineTuningAPI,
+    _litellm_fine_tuning_job_from_response,
+)
+from litellm.types.utils import LiteLLMFineTuningJob
 
 
 class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
@@ -12,6 +17,82 @@ class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
     AzureOpenAI methods to support fine tuning, inherits from OpenAIFineTuningAPI.
     """
 
+    @staticmethod
+    def _ensure_training_type(create_fine_tuning_job_data: Dict[str, Any]) -> None:
+        """
+        Azure requires trainingType in extra_body. Default to 1 (supervised) if omitted.
+        """
+        extra_body = create_fine_tuning_job_data.get("extra_body") or {}
+        if not isinstance(extra_body, dict):
+            extra_body = {}
+        if extra_body.get("trainingType") is None:
+            extra_body["trainingType"] = 1
+            create_fine_tuning_job_data["extra_body"] = extra_body
+            verbose_logger.debug(
+                "Azure fine-tuning: defaulting trainingType=1 (supervised)"
+            )
+
+    async def acreate_fine_tuning_job(
+        self,
+        create_fine_tuning_job_data: dict,
+        openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
+    ) -> LiteLLMFineTuningJob:
+        response = await openai_client.fine_tuning.jobs.create(
+            **create_fine_tuning_job_data
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
+    def create_fine_tuning_job(
+        self,
+        _is_async: bool,
+        create_fine_tuning_job_data: dict,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+    ) -> Union[LiteLLMFineTuningJob, Coroutine[Any, Any, LiteLLMFineTuningJob]]:
+        self._ensure_training_type(create_fine_tuning_job_data)
+
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+            api_version=api_version,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acreate_fine_tuning_job(
+                create_fine_tuning_job_data=create_fine_tuning_job_data,
+                openai_client=openai_client,
+            )
+
+        verbose_logger.debug(
+            "creating fine tuning job, args= %s", create_fine_tuning_job_data
+        )
+        response = cast(OpenAI, openai_client).fine_tuning.jobs.create(
+            **create_fine_tuning_job_data
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
     def get_openai_client(
         self,
         api_key: Optional[str],
diff --git a/litellm/llms/openai/fine_tuning/handler.py b/litellm/llms/openai/fine_tuning/handler.py
index 581ca1eea8c..2bb39aeaea4 100644
--- a/litellm/llms/openai/fine_tuning/handler.py
+++ b/litellm/llms/openai/fine_tuning/handler.py
@@ -7,7 +7,9 @@ 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]:
+def _normalize_fine_tuning_job_dict(
+    data: Dict[str, Any], is_azure: bool = False
+) -> Dict[str, Any]:
     """
     Normalize Azure OpenAI FineTuningJob response to match OpenAI schema.
 
@@ -16,6 +18,9 @@ def _normalize_fine_tuning_job_dict(data: Dict[str, Any]) -> Dict[str, Any]:
     - result_files: null → []
     - status: "pending" → "queued"
     """
+    if not is_azure:
+        return data
+
     normalized = data.copy()
 
     if normalized.get("organization_id") is None:
@@ -30,9 +35,11 @@ def _normalize_fine_tuning_job_dict(data: Dict[str, Any]) -> Dict[str, Any]:
     return normalized
 
 
-def _litellm_fine_tuning_job_from_response(response: Any) -> LiteLLMFineTuningJob:
+def _litellm_fine_tuning_job_from_response(
+    response: Any, is_azure: bool = False
+) -> LiteLLMFineTuningJob:
     return LiteLLMFineTuningJob(
-        **_normalize_fine_tuning_job_dict(response.model_dump())
+        **_normalize_fine_tuning_job_dict(response.model_dump(), is_azure=is_azure)
     )
 
 
diff --git a/tests/batches_tests/test_fine_tuning_api.py b/tests/batches_tests/test_fine_tuning_api.py
index b7ff8c7fae7..20867234e53 100644
--- a/tests/batches_tests/test_fine_tuning_api.py
+++ b/tests/batches_tests/test_fine_tuning_api.py
@@ -182,30 +182,17 @@ async def test_azure_create_fine_tune_jobs_async():
 
 def test_azure_trainingtype_defaults_to_one():
     """
-    Azure requires trainingType in extra_body. When omitted, LiteLLM defaults it to 1.
+    Azure requires trainingType in extra_body. When omitted, AzureOpenAIFineTuningAPI defaults it to 1.
     """
-    from unittest.mock import MagicMock, patch
-    from litellm.fine_tuning.main import create_fine_tuning_job
+    from litellm.llms.azure.fine_tuning.handler import AzureOpenAIFineTuningAPI
 
-    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"
-        )
+    handler = AzureOpenAIFineTuningAPI()
+    create_data = {"model": "gpt-4o-mini", "training_file": "file-test"}
 
-        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",
-        )
+    handler._ensure_training_type(create_data)
 
-        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
+    assert "extra_body" in create_data
+    assert create_data["extra_body"]["trainingType"] == 1
 
 
 @pytest.mark.asyncio()
diff --git a/tests/test_litellm/types/llms/test_types_llms_openai.py b/tests/test_litellm/types/llms/test_types_llms_openai.py
index 384d0dd73d7..323eb5a9424 100644
--- a/tests/test_litellm/types/llms/test_types_llms_openai.py
+++ b/tests/test_litellm/types/llms/test_types_llms_openai.py
@@ -424,8 +424,17 @@ 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"}
+        {"organization_id": None, "result_files": None, "status": "pending"},
+        is_azure=True,
     )
     assert out["organization_id"] == ""
     assert out["result_files"] == []
     assert out["status"] == "queued"
+
+
+def test_normalize_fine_tuning_job_dict_openai_unchanged():
+    from litellm.llms.openai.fine_tuning.handler import _normalize_fine_tuning_job_dict
+
+    data = {"organization_id": None, "result_files": None, "status": "pending"}
+    out = _normalize_fine_tuning_job_dict(data, is_azure=False)
+    assert out is data

From a9c7b17bfa47346dcf036e79bfb581551f09f96f Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Mon, 23 Mar 2026 12:38:33 +0530
Subject: [PATCH 3/6] address greptile review feedback (greploop iteration 2)

- Call _ensure_training_type in acreate_fine_tuning_job async override

Made-with: Cursor
---
 litellm/llms/azure/fine_tuning/handler.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/litellm/llms/azure/fine_tuning/handler.py b/litellm/llms/azure/fine_tuning/handler.py
index 2415c7d78ed..3d82172c585 100644
--- a/litellm/llms/azure/fine_tuning/handler.py
+++ b/litellm/llms/azure/fine_tuning/handler.py
@@ -37,6 +37,7 @@ class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
         create_fine_tuning_job_data: dict,
         openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
     ) -> LiteLLMFineTuningJob:
+        self._ensure_training_type(create_fine_tuning_job_data)
         response = await openai_client.fine_tuning.jobs.create(
             **create_fine_tuning_job_data
         )

From d4d91684cf77df72d9d45bfd20d83ae3054c8796 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Mon, 23 Mar 2026 12:44:55 +0530
Subject: [PATCH 4/6] address greptile review feedback (greploop iteration 3)

- Remove redundant _ensure_training_type call from acreate_fine_tuning_job
- Use explicit _AZURE_STATUS_MAP for status normalization

Made-with: Cursor
---
 litellm/llms/azure/fine_tuning/handler.py  |  1 -
 litellm/llms/openai/fine_tuning/handler.py | 11 ++++++++---
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/litellm/llms/azure/fine_tuning/handler.py b/litellm/llms/azure/fine_tuning/handler.py
index 3d82172c585..2415c7d78ed 100644
--- a/litellm/llms/azure/fine_tuning/handler.py
+++ b/litellm/llms/azure/fine_tuning/handler.py
@@ -37,7 +37,6 @@ class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
         create_fine_tuning_job_data: dict,
         openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
     ) -> LiteLLMFineTuningJob:
-        self._ensure_training_type(create_fine_tuning_job_data)
         response = await openai_client.fine_tuning.jobs.create(
             **create_fine_tuning_job_data
         )
diff --git a/litellm/llms/openai/fine_tuning/handler.py b/litellm/llms/openai/fine_tuning/handler.py
index 2bb39aeaea4..a9fbd88d2a8 100644
--- a/litellm/llms/openai/fine_tuning/handler.py
+++ b/litellm/llms/openai/fine_tuning/handler.py
@@ -6,6 +6,10 @@ from openai import AsyncAzureOpenAI, AsyncOpenAI, AzureOpenAI, OpenAI
 from litellm._logging import verbose_logger
 from litellm.types.utils import LiteLLMFineTuningJob
 
+_AZURE_STATUS_MAP = {
+    "pending": "queued",
+}
+
 
 def _normalize_fine_tuning_job_dict(
     data: Dict[str, Any], is_azure: bool = False
@@ -16,7 +20,7 @@ def _normalize_fine_tuning_job_dict(
     Azure differences:
     - organization_id: null → ""
     - result_files: null → []
-    - status: "pending" → "queued"
+    - status: mapped via _AZURE_STATUS_MAP
     """
     if not is_azure:
         return data
@@ -29,8 +33,9 @@ def _normalize_fine_tuning_job_dict(
     if normalized.get("result_files") is None:
         normalized["result_files"] = []
 
-    if normalized.get("status") == "pending":
-        normalized["status"] = "queued"
+    status = normalized.get("status")
+    if status in _AZURE_STATUS_MAP:
+        normalized["status"] = _AZURE_STATUS_MAP[status]
 
     return normalized
 

From 528bac5a2734693e2a8877a878f77cb36ad559ad Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Mon, 23 Mar 2026 12:54:33 +0530
Subject: [PATCH 5/6] feat(fine-tuning): address greptile review feedback
 (greploop iteration 4)

- Add cancel/retrieve overrides in AzureOpenAIFineTuningAPI to normalize responses
- Expand _AZURE_STATUS_MAP to handle all known Azure statuses
- Add "pending" to OpenAIFileObject.status allowed values
- Fix async test mock to return awaitable LiteLLMFineTuningJob
- Add test_openai_file_object_accepts_pending_status

Made-with: Cursor
---
 litellm/llms/azure/fine_tuning/handler.py     | 112 ++++++++++++++++++
 litellm/llms/openai/fine_tuning/handler.py    |   6 +
 litellm/types/llms/openai.py                  |   6 +-
 tests/batches_tests/test_fine_tuning_api.py   |   9 +-
 .../types/llms/test_types_llms_openai.py      |  15 +++
 5 files changed, 142 insertions(+), 6 deletions(-)

diff --git a/litellm/llms/azure/fine_tuning/handler.py b/litellm/llms/azure/fine_tuning/handler.py
index 2415c7d78ed..7e225a84454 100644
--- a/litellm/llms/azure/fine_tuning/handler.py
+++ b/litellm/llms/azure/fine_tuning/handler.py
@@ -42,6 +42,26 @@ class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
         )
         return _litellm_fine_tuning_job_from_response(response, is_azure=True)
 
+    async def acancel_fine_tuning_job(
+        self,
+        fine_tuning_job_id: str,
+        openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
+    ) -> LiteLLMFineTuningJob:
+        response = await openai_client.fine_tuning.jobs.cancel(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
+    async def aretrieve_fine_tuning_job(
+        self,
+        fine_tuning_job_id: str,
+        openai_client: Union[AsyncOpenAI, AsyncAzureOpenAI],
+    ) -> LiteLLMFineTuningJob:
+        response = await openai_client.fine_tuning.jobs.retrieve(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
     def create_fine_tuning_job(
         self,
         _is_async: bool,
@@ -93,6 +113,98 @@ class AzureOpenAIFineTuningAPI(OpenAIFineTuningAPI, BaseAzureLLM):
         )
         return _litellm_fine_tuning_job_from_response(response, is_azure=True)
 
+    def cancel_fine_tuning_job(
+        self,
+        _is_async: bool,
+        fine_tuning_job_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+    ) -> Union[LiteLLMFineTuningJob, Coroutine[Any, Any, LiteLLMFineTuningJob]]:
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+            api_version=api_version,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acancel_fine_tuning_job(
+                fine_tuning_job_id=fine_tuning_job_id,
+                openai_client=openai_client,
+            )
+
+        response = cast(OpenAI, openai_client).fine_tuning.jobs.cancel(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
+    def retrieve_fine_tuning_job(
+        self,
+        _is_async: bool,
+        fine_tuning_job_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        api_version: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = None,
+    ) -> Union[LiteLLMFineTuningJob, Coroutine[Any, Any, LiteLLMFineTuningJob]]:
+        openai_client: Optional[
+            Union[OpenAI, AsyncOpenAI, AzureOpenAI, AsyncAzureOpenAI]
+        ] = self.get_openai_client(
+            api_key=api_key,
+            api_base=api_base,
+            timeout=timeout,
+            max_retries=max_retries,
+            organization=organization,
+            client=client,
+            _is_async=_is_async,
+            api_version=api_version,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "Azure OpenAI client is not initialized. Make sure api_key is passed or AZURE_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, (AsyncOpenAI, AsyncAzureOpenAI)):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.aretrieve_fine_tuning_job(
+                fine_tuning_job_id=fine_tuning_job_id,
+                openai_client=openai_client,
+            )
+
+        response = cast(OpenAI, openai_client).fine_tuning.jobs.retrieve(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return _litellm_fine_tuning_job_from_response(response, is_azure=True)
+
     def get_openai_client(
         self,
         api_key: Optional[str],
diff --git a/litellm/llms/openai/fine_tuning/handler.py b/litellm/llms/openai/fine_tuning/handler.py
index a9fbd88d2a8..6800fe81d65 100644
--- a/litellm/llms/openai/fine_tuning/handler.py
+++ b/litellm/llms/openai/fine_tuning/handler.py
@@ -8,6 +8,12 @@ from litellm.types.utils import LiteLLMFineTuningJob
 
 _AZURE_STATUS_MAP = {
     "pending": "queued",
+    "notRunning": "queued",
+    "running": "running",
+    "succeeded": "succeeded",
+    "failed": "failed",
+    "canceled": "cancelled",
+    "canceling": "cancelled",
 }
 
 
diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py
index 5a80b40d61f..b9c8030c877 100644
--- a/litellm/types/llms/openai.py
+++ b/litellm/types/llms/openai.py
@@ -315,11 +315,11 @@ class OpenAIFileObject(BaseModel):
     `fine-tune`, `fine-tune-results`, `vision`, and `user_data`.
     """
 
-    status: Optional[Literal["uploaded", "processed", "error"]] = None
+    status: Optional[Literal["uploaded", "processed", "error", "pending"]] = None
     """Deprecated.
 
-    The current status of the file, which can be either `uploaded`, `processed`, or
-    `error`.
+    The current status of the file, which can be either `uploaded`, `processed`,
+    `error`, or `pending` (Azure may return `pending` immediately after upload).
     """
 
     expires_at: Optional[int] = None
diff --git a/tests/batches_tests/test_fine_tuning_api.py b/tests/batches_tests/test_fine_tuning_api.py
index 20867234e53..3ab15306fcb 100644
--- a/tests/batches_tests/test_fine_tuning_api.py
+++ b/tests/batches_tests/test_fine_tuning_api.py
@@ -616,11 +616,11 @@ async def test_mock_openai_retrieve_fine_tune_job():
 @pytest.mark.asyncio
 async def test_mock_azure_create_fine_tune_job_with_azure_specific_params():
     """Test that Azure-specific parameters are passed through extra_body"""
-    from openai import AsyncAzureOpenAI
     from openai.types.fine_tuning.fine_tuning_job import FineTuningJob
     from openai.types.fine_tuning.fine_tuning_job import Hyperparameters as OAIHyperparameters
+    from litellm.types.utils import LiteLLMFineTuningJob
 
-    mock_response = FineTuningJob(
+    mock_response = LiteLLMFineTuningJob(
         id="ft-azure-123",
         model="gpt-4.1-mini-2025-04-14",
         created_at=1677610602,
@@ -634,8 +634,11 @@ async def test_mock_azure_create_fine_tune_job_with_azure_specific_params():
         result_files=[],
     )
 
+    async def mock_async_create(*args, **kwargs):
+        return mock_response
+
     with patch("litellm.llms.azure.fine_tuning.handler.AzureOpenAIFineTuningAPI.create_fine_tuning_job") as mock_create:
-        mock_create.return_value = mock_response
+        mock_create.return_value = mock_async_create()
 
         response = await litellm.acreate_fine_tuning_job(
             model="gpt-4.1-mini-2025-04-14",
diff --git a/tests/test_litellm/types/llms/test_types_llms_openai.py b/tests/test_litellm/types/llms/test_types_llms_openai.py
index 323eb5a9424..569743269a5 100644
--- a/tests/test_litellm/types/llms/test_types_llms_openai.py
+++ b/tests/test_litellm/types/llms/test_types_llms_openai.py
@@ -438,3 +438,18 @@ def test_normalize_fine_tuning_job_dict_openai_unchanged():
     data = {"organization_id": None, "result_files": None, "status": "pending"}
     out = _normalize_fine_tuning_job_dict(data, is_azure=False)
     assert out is data
+
+
+def test_openai_file_object_accepts_pending_status():
+    from litellm.types.llms.openai import OpenAIFileObject
+
+    file_obj = OpenAIFileObject(
+        id="file-123",
+        bytes=1024,
+        created_at=1677610602,
+        filename="train.jsonl",
+        object="file",
+        purpose="fine-tune",
+        status="pending",
+    )
+    assert file_obj.status == "pending"

From e635cee712f7154c472f03c7ae4149ad167fc0d1 Mon Sep 17 00:00:00 2001
From: Sameer Kankute <sameer@berri.ai>
Date: Mon, 23 Mar 2026 13:03:10 +0530
Subject: [PATCH 6/6] feat(fine-tuning): address greptile review feedback
 (greploop iteration 5)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Remove unused FineTuningJob import from test
- Document "canceling" → "cancelled" mapping in _AZURE_STATUS_MAP

Made-with: Cursor
---
 litellm/llms/openai/fine_tuning/handler.py  | 2 ++
 tests/batches_tests/test_fine_tuning_api.py | 1 -
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/litellm/llms/openai/fine_tuning/handler.py b/litellm/llms/openai/fine_tuning/handler.py
index 6800fe81d65..c065325254e 100644
--- a/litellm/llms/openai/fine_tuning/handler.py
+++ b/litellm/llms/openai/fine_tuning/handler.py
@@ -15,6 +15,8 @@ _AZURE_STATUS_MAP = {
     "canceled": "cancelled",
     "canceling": "cancelled",
 }
+# Note: Azure's "canceling" (in-progress) is mapped to "cancelled" (terminal)
+# because LiteLLMFineTuningJob schema has no intermediate cancellation state.
 
 
 def _normalize_fine_tuning_job_dict(
diff --git a/tests/batches_tests/test_fine_tuning_api.py b/tests/batches_tests/test_fine_tuning_api.py
index 3ab15306fcb..7e220612ac6 100644
--- a/tests/batches_tests/test_fine_tuning_api.py
+++ b/tests/batches_tests/test_fine_tuning_api.py
@@ -616,7 +616,6 @@ async def test_mock_openai_retrieve_fine_tune_job():
 @pytest.mark.asyncio
 async def test_mock_azure_create_fine_tune_job_with_azure_specific_params():
     """Test that Azure-specific parameters are passed through extra_body"""
-    from openai.types.fine_tuning.fine_tuning_job import FineTuningJob
     from openai.types.fine_tuning.fine_tuning_job import Hyperparameters as OAIHyperparameters
     from litellm.types.utils import LiteLLMFineTuningJob