diff --git a/litellm/fine_tuning/main.py b/litellm/fine_tuning/main.py
new file mode 100644
index 000000000..6fa2bd9fb
--- /dev/null
+++ b/litellm/fine_tuning/main.py
@@ -0,0 +1,182 @@
+"""
+Main File for Fine Tuning API implementation
+
+https://platform.openai.com/docs/api-reference/fine-tuning
+
+- fine_tuning.jobs.create()
+- fine_tuning.jobs.list()
+- client.fine_tuning.jobs.list_events()
+"""
+
+import asyncio
+import contextvars
+import os
+from functools import partial
+from typing import Any, Coroutine, Dict, Literal, Optional, Union
+
+import httpx
+
+import litellm
+from litellm.llms.openai_fine_tuning.openai import (
+    FineTuningJob,
+    FineTuningJobCreate,
+    OpenAIFineTuningAPI,
+)
+from litellm.types.llms.openai import Hyperparameters
+from litellm.types.router import *
+from litellm.utils import supports_httpx_timeout
+
+####### ENVIRONMENT VARIABLES ###################
+openai_fine_tuning_instance = OpenAIFineTuningAPI()
+#################################################
+
+
+async def acreate_fine_tuning_job(
+    model: str,
+    training_file: str,
+    hyperparameters: Optional[Hyperparameters] = None,
+    suffix: Optional[str] = None,
+    validation_file: Optional[str] = None,
+    integrations: Optional[List[str]] = None,
+    seed: Optional[int] = None,
+    custom_llm_provider: Literal["openai"] = "openai",
+    extra_headers: Optional[Dict[str, str]] = None,
+    extra_body: Optional[Dict[str, str]] = None,
+    **kwargs,
+) -> FineTuningJob:
+    """
+    Async: Creates and executes a batch from an uploaded file of request
+
+    LiteLLM Equivalent of POST: https://api.openai.com/v1/batches
+    """
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["acreate_fine_tuning_job"] = True
+
+        # Use a partial function to pass your keyword arguments
+        func = partial(
+            create_fine_tuning_job,
+            model,
+            training_file,
+            hyperparameters,
+            suffix,
+            validation_file,
+            integrations,
+            seed,
+            custom_llm_provider,
+            extra_headers,
+            extra_body,
+            **kwargs,
+        )
+
+        # Add the context to the function
+        ctx = contextvars.copy_context()
+        func_with_context = partial(ctx.run, func)
+        init_response = await loop.run_in_executor(None, func_with_context)
+        if asyncio.iscoroutine(init_response):
+            response = await init_response
+        else:
+            response = init_response  # type: ignore
+        return response
+    except Exception as e:
+        raise e
+
+
+def create_fine_tuning_job(
+    model: str,
+    training_file: str,
+    hyperparameters: Optional[Hyperparameters] = None,
+    suffix: Optional[str] = None,
+    validation_file: Optional[str] = None,
+    integrations: Optional[List[str]] = None,
+    seed: Optional[int] = None,
+    custom_llm_provider: Literal["openai"] = "openai",
+    extra_headers: Optional[Dict[str, str]] = None,
+    extra_body: Optional[Dict[str, str]] = None,
+    **kwargs,
+) -> Union[FineTuningJob, Coroutine[Any, Any, FineTuningJob]]:
+    """
+    Creates a fine-tuning job which begins the process of creating a new model from a given dataset.
+
+    Response includes details of the enqueued job including job status and the name of the fine-tuned models once complete
+
+    """
+    try:
+        optional_params = GenericLiteLLMParams(**kwargs)
+        if custom_llm_provider == "openai":
+
+            # for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
+            api_base = (
+                optional_params.api_base
+                or litellm.api_base
+                or os.getenv("OPENAI_API_BASE")
+                or "https://api.openai.com/v1"
+            )
+            organization = (
+                optional_params.organization
+                or litellm.organization
+                or os.getenv("OPENAI_ORGANIZATION", None)
+                or None  # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
+            )
+            # set API KEY
+            api_key = (
+                optional_params.api_key
+                or litellm.api_key  # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
+                or litellm.openai_key
+                or os.getenv("OPENAI_API_KEY")
+            )
+            ### TIMEOUT LOGIC ###
+            timeout = (
+                optional_params.timeout or kwargs.get("request_timeout", 600) or 600
+            )
+            # set timeout for 10 minutes by default
+
+            if (
+                timeout is not None
+                and isinstance(timeout, httpx.Timeout)
+                and supports_httpx_timeout(custom_llm_provider) == False
+            ):
+                read_timeout = timeout.read or 600
+                timeout = read_timeout  # default 10 min timeout
+            elif timeout is not None and not isinstance(timeout, httpx.Timeout):
+                timeout = float(timeout)  # type: ignore
+            elif timeout is None:
+                timeout = 600.0
+
+            _is_async = kwargs.pop("acreate_fine_tuning_job", False) is True
+
+            create_fine_tuning_job_data = FineTuningJobCreate(
+                model=model,
+                training_file=training_file,
+                hyperparameters=hyperparameters,
+                suffix=suffix,
+                validation_file=validation_file,
+                integrations=integrations,
+                seed=seed,
+            )
+
+            response = openai_fine_tuning_instance.create_fine_tuning_job(
+                api_base=api_base,
+                api_key=api_key,
+                organization=organization,
+                create_fine_tuning_job_data=create_fine_tuning_job_data,
+                timeout=timeout,
+                max_retries=optional_params.max_retries,
+                _is_async=_is_async,
+            )
+        else:
+            raise litellm.exceptions.BadRequestError(
+                message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
+                    custom_llm_provider
+                ),
+                model="n/a",
+                llm_provider=custom_llm_provider,
+                response=httpx.Response(
+                    status_code=400,
+                    content="Unsupported provider",
+                    request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"),  # type: ignore
+                ),
+            )
+        return response
+    except Exception as e:
+        raise e