Merge pull request #4956 from BerriAI/litellm_add_finetuning_Endpoints

[Feat] Add `litellm.create_fine_tuning_job()` , `litellm.list_fine_tuning_jobs()`, `litellm.cancel_fine_tuning_job()` finetuning endpoints

litellm/__init__.py

@@ -906,6 +906,7 @@ from .proxy.proxy_cli import run_server
 from .router import Router
 from .assistants.main import *
 from .batches.main import *
+from .fine_tuning.main import *
 from .files.main import *
 from .scheduler import *
 from .cost_calculator import response_cost_calculator, cost_per_token

litellm/fine_tuning/main.py

@@ -0,0 +1,429 @@
+"""
+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] = {},  # type: ignore
+    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
+
+    """
+    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] = {},  # type: ignore
+    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
+
+
+async def acancel_fine_tuning_job(
+    fine_tuning_job_id: str,
+    custom_llm_provider: Literal["openai"] = "openai",
+    extra_headers: Optional[Dict[str, str]] = None,
+    extra_body: Optional[Dict[str, str]] = None,
+    **kwargs,
+) -> FineTuningJob:
+    """
+    Async: Immediately cancel a fine-tune job.
+    """
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["acancel_fine_tuning_job"] = True
+
+        # Use a partial function to pass your keyword arguments
+        func = partial(
+            cancel_fine_tuning_job,
+            fine_tuning_job_id,
+            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 cancel_fine_tuning_job(
+    fine_tuning_job_id: str,
+    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]]:
+    """
+    Immediately cancel a fine-tune job.
+
+    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("acancel_fine_tuning_job", False) is True
+
+            response = openai_fine_tuning_instance.cancel_fine_tuning_job(
+                api_base=api_base,
+                api_key=api_key,
+                organization=organization,
+                fine_tuning_job_id=fine_tuning_job_id,
+                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
+
+
+async def alist_fine_tuning_jobs(
+    after: Optional[str] = None,
+    limit: 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: List your organization's fine-tuning jobs
+    """
+    try:
+        loop = asyncio.get_event_loop()
+        kwargs["alist_fine_tuning_jobs"] = True
+
+        # Use a partial function to pass your keyword arguments
+        func = partial(
+            list_fine_tuning_jobs,
+            after,
+            limit,
+            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 list_fine_tuning_jobs(
+    after: Optional[str] = None,
+    limit: Optional[int] = None,
+    custom_llm_provider: Literal["openai"] = "openai",
+    extra_headers: Optional[Dict[str, str]] = None,
+    extra_body: Optional[Dict[str, str]] = None,
+    **kwargs,
+):
+    """
+    List your organization's fine-tuning jobs
+
+    Params:
+
+    - after: Optional[str] = None, Identifier for the last job from the previous pagination request.
+    - limit: Optional[int] = None, Number of fine-tuning jobs to retrieve. Defaults to 20
+    """
+    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("alist_fine_tuning_jobs", False) is True
+
+            response = openai_fine_tuning_instance.list_fine_tuning_jobs(
+                api_base=api_base,
+                api_key=api_key,
+                organization=organization,
+                after=after,
+                limit=limit,
+                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

litellm/llms/openai_fine_tuning/openai.py

@@ -0,0 +1,199 @@
+from typing import Any, Coroutine, Optional, Union
+
+import httpx
+from openai import AsyncOpenAI, OpenAI
+from openai.pagination import AsyncCursorPage
+from openai.types.fine_tuning import FineTuningJob
+
+from litellm._logging import verbose_logger
+from litellm.llms.base import BaseLLM
+from litellm.types.llms.openai import FineTuningJobCreate
+
+
+class OpenAIFineTuningAPI(BaseLLM):
+    """
+    OpenAI methods to support for batches
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def get_openai_client(
+        self,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+        _is_async: bool = False,
+    ) -> Optional[Union[OpenAI, AsyncOpenAI]]:
+        received_args = locals()
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = None
+        if client is None:
+            data = {}
+            for k, v in received_args.items():
+                if k == "self" or k == "client" or k == "_is_async":
+                    pass
+                elif k == "api_base" and v is not None:
+                    data["base_url"] = v
+                elif v is not None:
+                    data[k] = v
+            if _is_async is True:
+                openai_client = AsyncOpenAI(**data)
+            else:
+                openai_client = OpenAI(**data)  # type: ignore
+        else:
+            openai_client = client
+
+        return openai_client
+
+    async def acreate_fine_tuning_job(
+        self,
+        create_fine_tuning_job_data: FineTuningJobCreate,
+        openai_client: AsyncOpenAI,
+    ) -> FineTuningJob:
+        response = await openai_client.fine_tuning.jobs.create(
+            **create_fine_tuning_job_data  # type: ignore
+        )
+        return response
+
+    def create_fine_tuning_job(
+        self,
+        _is_async: bool,
+        create_fine_tuning_job_data: FineTuningJobCreate,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+    ) -> Union[FineTuningJob, Union[Coroutine[Any, Any, FineTuningJob]]]:
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = 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,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acreate_fine_tuning_job(  # type: ignore
+                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 = openai_client.fine_tuning.jobs.create(**create_fine_tuning_job_data)  # type: ignore
+        return response
+
+    async def acancel_fine_tuning_job(
+        self,
+        fine_tuning_job_id: str,
+        openai_client: AsyncOpenAI,
+    ) -> FineTuningJob:
+        response = await openai_client.fine_tuning.jobs.cancel(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return response
+
+    def cancel_fine_tuning_job(
+        self,
+        _is_async: bool,
+        fine_tuning_job_id: str,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+    ):
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = 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,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.acancel_fine_tuning_job(  # type: ignore
+                fine_tuning_job_id=fine_tuning_job_id,
+                openai_client=openai_client,
+            )
+        verbose_logger.debug("canceling fine tuning job, args= %s", fine_tuning_job_id)
+        response = openai_client.fine_tuning.jobs.cancel(
+            fine_tuning_job_id=fine_tuning_job_id
+        )
+        return response
+
+    async def alist_fine_tuning_jobs(
+        self,
+        openai_client: AsyncOpenAI,
+        after: Optional[str] = None,
+        limit: Optional[int] = None,
+    ):
+        response = await openai_client.fine_tuning.jobs.list(after=after, limit=limit)  # type: ignore
+        return response
+
+    def list_fine_tuning_jobs(
+        self,
+        _is_async: bool,
+        api_key: Optional[str],
+        api_base: Optional[str],
+        timeout: Union[float, httpx.Timeout],
+        max_retries: Optional[int],
+        organization: Optional[str],
+        client: Optional[Union[OpenAI, AsyncOpenAI]] = None,
+        after: Optional[str] = None,
+        limit: Optional[int] = None,
+    ):
+        openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = 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,
+        )
+        if openai_client is None:
+            raise ValueError(
+                "OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
+            )
+
+        if _is_async is True:
+            if not isinstance(openai_client, AsyncOpenAI):
+                raise ValueError(
+                    "OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
+                )
+            return self.alist_fine_tuning_jobs(  # type: ignore
+                after=after,
+                limit=limit,
+                openai_client=openai_client,
+            )
+        verbose_logger.debug("list fine tuning job, after= %s, limit= %s", after, limit)
+        response = openai_client.fine_tuning.jobs.list(after=after, limit=limit)  # type: ignore
+        return response
+        pass

litellm/tests/test_fine_tuning_api.py

@@ -0,0 +1,114 @@
+import os
+import sys
+import traceback
+
+import pytest
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+from openai import APITimeoutError as Timeout
+
+import litellm
+
+litellm.num_retries = 0
+import logging
+
+from litellm import create_fine_tuning_job
+from litellm._logging import verbose_logger
+
+
+def test_create_fine_tune_job():
+    verbose_logger.setLevel(logging.DEBUG)
+    file_name = "openai_batch_completions.jsonl"
+    _current_dir = os.path.dirname(os.path.abspath(__file__))
+    file_path = os.path.join(_current_dir, file_name)
+
+    file_obj = litellm.create_file(
+        file=open(file_path, "rb"),
+        purpose="fine-tune",
+        custom_llm_provider="openai",
+    )
+    print("Response from creating file=", file_obj)
+
+    create_fine_tuning_response = litellm.create_fine_tuning_job(
+        model="gpt-3.5-turbo-0125",
+        training_file=file_obj.id,
+    )
+
+    print("response from litellm.create_fine_tuning_job=", create_fine_tuning_response)
+
+    assert create_fine_tuning_response.id is not None
+    assert create_fine_tuning_response.model == "gpt-3.5-turbo-0125"
+
+    # list fine tuning jobs
+    print("listing ft jobs")
+    ft_jobs = litellm.list_fine_tuning_jobs(limit=2)
+    print("response from litellm.list_fine_tuning_jobs=", ft_jobs)
+
+    assert len(list(ft_jobs)) > 0
+
+    # delete file
+
+    litellm.file_delete(
+        file_id=file_obj.id,
+    )
+
+    # cancel ft job
+    response = litellm.cancel_fine_tuning_job(
+        fine_tuning_job_id=create_fine_tuning_response.id,
+    )
+
+    print("response from litellm.cancel_fine_tuning_job=", response)
+
+    assert response.status == "cancelled"
+    assert response.id == create_fine_tuning_response.id
+    pass
+
+
+@pytest.mark.asyncio
+async def test_create_fine_tune_jobs_async():
+    verbose_logger.setLevel(logging.DEBUG)
+    file_name = "openai_batch_completions.jsonl"
+    _current_dir = os.path.dirname(os.path.abspath(__file__))
+    file_path = os.path.join(_current_dir, file_name)
+
+    file_obj = await litellm.acreate_file(
+        file=open(file_path, "rb"),
+        purpose="fine-tune",
+        custom_llm_provider="openai",
+    )
+    print("Response from creating file=", file_obj)
+
+    create_fine_tuning_response = await litellm.acreate_fine_tuning_job(
+        model="gpt-3.5-turbo-0125",
+        training_file=file_obj.id,
+    )
+
+    print("response from litellm.create_fine_tuning_job=", create_fine_tuning_response)
+
+    assert create_fine_tuning_response.id is not None
+    assert create_fine_tuning_response.model == "gpt-3.5-turbo-0125"
+
+    # list fine tuning jobs
+    print("listing ft jobs")
+    ft_jobs = await litellm.alist_fine_tuning_jobs(limit=2)
+    print("response from litellm.list_fine_tuning_jobs=", ft_jobs)
+    assert len(list(ft_jobs)) > 0
+
+    # delete file
+
+    await litellm.afile_delete(
+        file_id=file_obj.id,
+    )
+
+    # cancel ft job
+    response = await litellm.acancel_fine_tuning_job(
+        fine_tuning_job_id=create_fine_tuning_response.id,
+    )
+
+    print("response from litellm.cancel_fine_tuning_job=", response)
+
+    assert response.status == "cancelled"
+    assert response.id == create_fine_tuning_response.id
+    pass

litellm/types/llms/openai.py

@@ -30,7 +30,7 @@ from openai.types.beta.thread_create_params import (
 from openai.types.beta.threads.message import Message as OpenAIMessage
 from openai.types.beta.threads.message_content import MessageContent
 from openai.types.beta.threads.run import Run
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from typing_extensions import Dict, Required, override
 
 FileContent = Union[IO[bytes], bytes, PathLike]
@@ -455,3 +455,50 @@ class ChatCompletionUsageBlock(TypedDict):
     prompt_tokens: int
     completion_tokens: int
     total_tokens: int
+
+
+class Hyperparameters(TypedDict):
+    batch_size: Optional[Union[str, int]]  # "Number of examples in each batch."
+    learning_rate_multiplier: Optional[
+        Union[str, float]
+    ]  # Scaling factor for the learning rate
+    n_epochs: Optional[Union[str, int]]  # "The number of epochs to train the model for"
+
+
+class FineTuningJobCreate(TypedDict):
+    """
+    FineTuningJobCreate - Create a fine-tuning job
+
+    Example Request
+    ```
+    {
+        "model": "gpt-3.5-turbo",
+        "training_file": "file-abc123",
+        "hyperparameters": {
+            "batch_size": "auto",
+            "learning_rate_multiplier": 0.1,
+            "n_epochs": 3
+        },
+        "suffix": "custom-model-name",
+        "validation_file": "file-xyz789",
+        "integrations": ["slack"],
+        "seed": 42
+    }
+    ```
+    """
+
+    model: str  # "The name of the model to fine-tune."
+    training_file: str  # "The ID of an uploaded file that contains training data."
+    hyperparameters: Optional[
+        Hyperparameters
+    ]  # "The hyperparameters used for the fine-tuning job."
+    suffix: Optional[
+        str
+    ]  # "A string of up to 18 characters that will be added to your fine-tuned model name."
+    validation_file: Optional[
+        str
+    ]  # "The ID of an uploaded file that contains validation data."
+    integrations: Optional[
+        List[str]
+    ]  # "A list of integrations to enable for your fine-tuning job."
+    seed: Optional[int]  # "The seed controls the reproducibility of the job."