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new file mode 100644
index 0000000000000000000000000000000000000000..92b81329b1aab9a00f567285a4f1d7c92ab59b27
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/__init__.py
@@ -0,0 +1,14 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from .fine_tuning_job import FineTuningJob as FineTuningJob
+from .job_list_params import JobListParams as JobListParams
+from .job_create_params import JobCreateParams as JobCreateParams
+from .fine_tuning_job_event import FineTuningJobEvent as FineTuningJobEvent
+from .job_list_events_params import JobListEventsParams as JobListEventsParams
+from .fine_tuning_job_integration import FineTuningJobIntegration as FineTuningJobIntegration
+from .fine_tuning_job_wandb_integration import FineTuningJobWandbIntegration as FineTuningJobWandbIntegration
+from .fine_tuning_job_wandb_integration_object import (
+    FineTuningJobWandbIntegrationObject as FineTuningJobWandbIntegrationObject,
+)
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diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5a11c2107108874881799865b2c1af8273810cf
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job.py
@@ -0,0 +1,212 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import List, Union, Optional
+from typing_extensions import Literal
+
+from ..._models import BaseModel
+from .fine_tuning_job_wandb_integration_object import FineTuningJobWandbIntegrationObject
+
+__all__ = [
+    "FineTuningJob",
+    "Error",
+    "Hyperparameters",
+    "Method",
+    "MethodDpo",
+    "MethodDpoHyperparameters",
+    "MethodSupervised",
+    "MethodSupervisedHyperparameters",
+]
+
+
+class Error(BaseModel):
+    code: str
+    """A machine-readable error code."""
+
+    message: str
+    """A human-readable error message."""
+
+    param: Optional[str] = None
+    """The parameter that was invalid, usually `training_file` or `validation_file`.
+
+    This field will be null if the failure was not parameter-specific.
+    """
+
+
+class Hyperparameters(BaseModel):
+    batch_size: Union[Literal["auto"], int, None] = None
+    """Number of examples in each batch.
+
+    A larger batch size means that model parameters are updated less frequently, but
+    with lower variance.
+    """
+
+    learning_rate_multiplier: Union[Literal["auto"], float, None] = None
+    """Scaling factor for the learning rate.
+
+    A smaller learning rate may be useful to avoid overfitting.
+    """
+
+    n_epochs: Union[Literal["auto"], int, None] = None
+    """The number of epochs to train the model for.
+
+    An epoch refers to one full cycle through the training dataset.
+    """
+
+
+class MethodDpoHyperparameters(BaseModel):
+    batch_size: Union[Literal["auto"], int, None] = None
+    """Number of examples in each batch.
+
+    A larger batch size means that model parameters are updated less frequently, but
+    with lower variance.
+    """
+
+    beta: Union[Literal["auto"], float, None] = None
+    """The beta value for the DPO method.
+
+    A higher beta value will increase the weight of the penalty between the policy
+    and reference model.
+    """
+
+    learning_rate_multiplier: Union[Literal["auto"], float, None] = None
+    """Scaling factor for the learning rate.
+
+    A smaller learning rate may be useful to avoid overfitting.
+    """
+
+    n_epochs: Union[Literal["auto"], int, None] = None
+    """The number of epochs to train the model for.
+
+    An epoch refers to one full cycle through the training dataset.
+    """
+
+
+class MethodDpo(BaseModel):
+    hyperparameters: Optional[MethodDpoHyperparameters] = None
+    """The hyperparameters used for the fine-tuning job."""
+
+
+class MethodSupervisedHyperparameters(BaseModel):
+    batch_size: Union[Literal["auto"], int, None] = None
+    """Number of examples in each batch.
+
+    A larger batch size means that model parameters are updated less frequently, but
+    with lower variance.
+    """
+
+    learning_rate_multiplier: Union[Literal["auto"], float, None] = None
+    """Scaling factor for the learning rate.
+
+    A smaller learning rate may be useful to avoid overfitting.
+    """
+
+    n_epochs: Union[Literal["auto"], int, None] = None
+    """The number of epochs to train the model for.
+
+    An epoch refers to one full cycle through the training dataset.
+    """
+
+
+class MethodSupervised(BaseModel):
+    hyperparameters: Optional[MethodSupervisedHyperparameters] = None
+    """The hyperparameters used for the fine-tuning job."""
+
+
+class Method(BaseModel):
+    dpo: Optional[MethodDpo] = None
+    """Configuration for the DPO fine-tuning method."""
+
+    supervised: Optional[MethodSupervised] = None
+    """Configuration for the supervised fine-tuning method."""
+
+    type: Optional[Literal["supervised", "dpo"]] = None
+    """The type of method. Is either `supervised` or `dpo`."""
+
+
+class FineTuningJob(BaseModel):
+    id: str
+    """The object identifier, which can be referenced in the API endpoints."""
+
+    created_at: int
+    """The Unix timestamp (in seconds) for when the fine-tuning job was created."""
+
+    error: Optional[Error] = None
+    """
+    For fine-tuning jobs that have `failed`, this will contain more information on
+    the cause of the failure.
+    """
+
+    fine_tuned_model: Optional[str] = None
+    """The name of the fine-tuned model that is being created.
+
+    The value will be null if the fine-tuning job is still running.
+    """
+
+    finished_at: Optional[int] = None
+    """The Unix timestamp (in seconds) for when the fine-tuning job was finished.
+
+    The value will be null if the fine-tuning job is still running.
+    """
+
+    hyperparameters: Hyperparameters
+    """The hyperparameters used for the fine-tuning job.
+
+    This value will only be returned when running `supervised` jobs.
+    """
+
+    model: str
+    """The base model that is being fine-tuned."""
+
+    object: Literal["fine_tuning.job"]
+    """The object type, which is always "fine_tuning.job"."""
+
+    organization_id: str
+    """The organization that owns the fine-tuning job."""
+
+    result_files: List[str]
+    """The compiled results file ID(s) for the fine-tuning job.
+
+    You can retrieve the results with the
+    [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
+    """
+
+    seed: int
+    """The seed used for the fine-tuning job."""
+
+    status: Literal["validating_files", "queued", "running", "succeeded", "failed", "cancelled"]
+    """
+    The current status of the fine-tuning job, which can be either
+    `validating_files`, `queued`, `running`, `succeeded`, `failed`, or `cancelled`.
+    """
+
+    trained_tokens: Optional[int] = None
+    """The total number of billable tokens processed by this fine-tuning job.
+
+    The value will be null if the fine-tuning job is still running.
+    """
+
+    training_file: str
+    """The file ID used for training.
+
+    You can retrieve the training data with the
+    [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
+    """
+
+    validation_file: Optional[str] = None
+    """The file ID used for validation.
+
+    You can retrieve the validation results with the
+    [Files API](https://platform.openai.com/docs/api-reference/files/retrieve-contents).
+    """
+
+    estimated_finish: Optional[int] = None
+    """
+    The Unix timestamp (in seconds) for when the fine-tuning job is estimated to
+    finish. The value will be null if the fine-tuning job is not running.
+    """
+
+    integrations: Optional[List[FineTuningJobWandbIntegrationObject]] = None
+    """A list of integrations to enable for this fine-tuning job."""
+
+    method: Optional[Method] = None
+    """The method used for fine-tuning."""
diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_event.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_event.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d728bd765d03d8d47aa28de866aacd0b1e694c6
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_event.py
@@ -0,0 +1,32 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+import builtins
+from typing import Optional
+from typing_extensions import Literal
+
+from ..._models import BaseModel
+
+__all__ = ["FineTuningJobEvent"]
+
+
+class FineTuningJobEvent(BaseModel):
+    id: str
+    """The object identifier."""
+
+    created_at: int
+    """The Unix timestamp (in seconds) for when the fine-tuning job was created."""
+
+    level: Literal["info", "warn", "error"]
+    """The log level of the event."""
+
+    message: str
+    """The message of the event."""
+
+    object: Literal["fine_tuning.job.event"]
+    """The object type, which is always "fine_tuning.job.event"."""
+
+    data: Optional[builtins.object] = None
+    """The data associated with the event."""
+
+    type: Optional[Literal["message", "metrics"]] = None
+    """The type of event."""
diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_integration.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_integration.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a66aa4f17e671b1d1366f10f3b17645529a79b1
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_integration.py
@@ -0,0 +1,6 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+
+from .fine_tuning_job_wandb_integration_object import FineTuningJobWandbIntegrationObject
+
+FineTuningJobIntegration = FineTuningJobWandbIntegrationObject
diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_wandb_integration.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_wandb_integration.py
new file mode 100644
index 0000000000000000000000000000000000000000..4ac282eb5411a80b327605af2f7e14039bd13ebe
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_wandb_integration.py
@@ -0,0 +1,33 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import List, Optional
+
+from ..._models import BaseModel
+
+__all__ = ["FineTuningJobWandbIntegration"]
+
+
+class FineTuningJobWandbIntegration(BaseModel):
+    project: str
+    """The name of the project that the new run will be created under."""
+
+    entity: Optional[str] = None
+    """The entity to use for the run.
+
+    This allows you to set the team or username of the WandB user that you would
+    like associated with the run. If not set, the default entity for the registered
+    WandB API key is used.
+    """
+
+    name: Optional[str] = None
+    """A display name to set for the run.
+
+    If not set, we will use the Job ID as the name.
+    """
+
+    tags: Optional[List[str]] = None
+    """A list of tags to be attached to the newly created run.
+
+    These tags are passed through directly to WandB. Some default tags are generated
+    by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".
+    """
diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_wandb_integration_object.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_wandb_integration_object.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b94354d505ca790cc96a9c9b461bfae0672ea34
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/fine_tuning_job_wandb_integration_object.py
@@ -0,0 +1,21 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing_extensions import Literal
+
+from ..._models import BaseModel
+from .fine_tuning_job_wandb_integration import FineTuningJobWandbIntegration
+
+__all__ = ["FineTuningJobWandbIntegrationObject"]
+
+
+class FineTuningJobWandbIntegrationObject(BaseModel):
+    type: Literal["wandb"]
+    """The type of the integration being enabled for the fine-tuning job"""
+
+    wandb: FineTuningJobWandbIntegration
+    """The settings for your integration with Weights and Biases.
+
+    This payload specifies the project that metrics will be sent to. Optionally, you
+    can set an explicit display name for your run, add tags to your run, and set a
+    default entity (team, username, etc) to be associated with your run.
+    """
diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/job_create_params.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/job_create_params.py
new file mode 100644
index 0000000000000000000000000000000000000000..09c3f8571c23f9d98fc90958032c27961891a6a8
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/job_create_params.py
@@ -0,0 +1,224 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing import List, Union, Iterable, Optional
+from typing_extensions import Literal, Required, TypedDict
+
+__all__ = [
+    "JobCreateParams",
+    "Hyperparameters",
+    "Integration",
+    "IntegrationWandb",
+    "Method",
+    "MethodDpo",
+    "MethodDpoHyperparameters",
+    "MethodSupervised",
+    "MethodSupervisedHyperparameters",
+]
+
+
+class JobCreateParams(TypedDict, total=False):
+    model: Required[Union[str, Literal["babbage-002", "davinci-002", "gpt-3.5-turbo", "gpt-4o-mini"]]]
+    """The name of the model to fine-tune.
+
+    You can select one of the
+    [supported models](https://platform.openai.com/docs/guides/fine-tuning#which-models-can-be-fine-tuned).
+    """
+
+    training_file: Required[str]
+    """The ID of an uploaded file that contains training data.
+
+    See [upload file](https://platform.openai.com/docs/api-reference/files/create)
+    for how to upload a file.
+
+    Your dataset must be formatted as a JSONL file. Additionally, you must upload
+    your file with the purpose `fine-tune`.
+
+    The contents of the file should differ depending on if the model uses the
+    [chat](https://platform.openai.com/docs/api-reference/fine-tuning/chat-input),
+    [completions](https://platform.openai.com/docs/api-reference/fine-tuning/completions-input)
+    format, or if the fine-tuning method uses the
+    [preference](https://platform.openai.com/docs/api-reference/fine-tuning/preference-input)
+    format.
+
+    See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning)
+    for more details.
+    """
+
+    hyperparameters: Hyperparameters
+    """
+    The hyperparameters used for the fine-tuning job. This value is now deprecated
+    in favor of `method`, and should be passed in under the `method` parameter.
+    """
+
+    integrations: Optional[Iterable[Integration]]
+    """A list of integrations to enable for your fine-tuning job."""
+
+    method: Method
+    """The method used for fine-tuning."""
+
+    seed: Optional[int]
+    """The seed controls the reproducibility of the job.
+
+    Passing in the same seed and job parameters should produce the same results, but
+    may differ in rare cases. If a seed is not specified, one will be generated for
+    you.
+    """
+
+    suffix: Optional[str]
+    """
+    A string of up to 64 characters that will be added to your fine-tuned model
+    name.
+
+    For example, a `suffix` of "custom-model-name" would produce a model name like
+    `ft:gpt-4o-mini:openai:custom-model-name:7p4lURel`.
+    """
+
+    validation_file: Optional[str]
+    """The ID of an uploaded file that contains validation data.
+
+    If you provide this file, the data is used to generate validation metrics
+    periodically during fine-tuning. These metrics can be viewed in the fine-tuning
+    results file. The same data should not be present in both train and validation
+    files.
+
+    Your dataset must be formatted as a JSONL file. You must upload your file with
+    the purpose `fine-tune`.
+
+    See the [fine-tuning guide](https://platform.openai.com/docs/guides/fine-tuning)
+    for more details.
+    """
+
+
+class Hyperparameters(TypedDict, total=False):
+    batch_size: Union[Literal["auto"], int]
+    """Number of examples in each batch.
+
+    A larger batch size means that model parameters are updated less frequently, but
+    with lower variance.
+    """
+
+    learning_rate_multiplier: Union[Literal["auto"], float]
+    """Scaling factor for the learning rate.
+
+    A smaller learning rate may be useful to avoid overfitting.
+    """
+
+    n_epochs: Union[Literal["auto"], int]
+    """The number of epochs to train the model for.
+
+    An epoch refers to one full cycle through the training dataset.
+    """
+
+
+class IntegrationWandb(TypedDict, total=False):
+    project: Required[str]
+    """The name of the project that the new run will be created under."""
+
+    entity: Optional[str]
+    """The entity to use for the run.
+
+    This allows you to set the team or username of the WandB user that you would
+    like associated with the run. If not set, the default entity for the registered
+    WandB API key is used.
+    """
+
+    name: Optional[str]
+    """A display name to set for the run.
+
+    If not set, we will use the Job ID as the name.
+    """
+
+    tags: List[str]
+    """A list of tags to be attached to the newly created run.
+
+    These tags are passed through directly to WandB. Some default tags are generated
+    by OpenAI: "openai/finetune", "openai/{base-model}", "openai/{ftjob-abcdef}".
+    """
+
+
+class Integration(TypedDict, total=False):
+    type: Required[Literal["wandb"]]
+    """The type of integration to enable.
+
+    Currently, only "wandb" (Weights and Biases) is supported.
+    """
+
+    wandb: Required[IntegrationWandb]
+    """The settings for your integration with Weights and Biases.
+
+    This payload specifies the project that metrics will be sent to. Optionally, you
+    can set an explicit display name for your run, add tags to your run, and set a
+    default entity (team, username, etc) to be associated with your run.
+    """
+
+
+class MethodDpoHyperparameters(TypedDict, total=False):
+    batch_size: Union[Literal["auto"], int]
+    """Number of examples in each batch.
+
+    A larger batch size means that model parameters are updated less frequently, but
+    with lower variance.
+    """
+
+    beta: Union[Literal["auto"], float]
+    """The beta value for the DPO method.
+
+    A higher beta value will increase the weight of the penalty between the policy
+    and reference model.
+    """
+
+    learning_rate_multiplier: Union[Literal["auto"], float]
+    """Scaling factor for the learning rate.
+
+    A smaller learning rate may be useful to avoid overfitting.
+    """
+
+    n_epochs: Union[Literal["auto"], int]
+    """The number of epochs to train the model for.
+
+    An epoch refers to one full cycle through the training dataset.
+    """
+
+
+class MethodDpo(TypedDict, total=False):
+    hyperparameters: MethodDpoHyperparameters
+    """The hyperparameters used for the fine-tuning job."""
+
+
+class MethodSupervisedHyperparameters(TypedDict, total=False):
+    batch_size: Union[Literal["auto"], int]
+    """Number of examples in each batch.
+
+    A larger batch size means that model parameters are updated less frequently, but
+    with lower variance.
+    """
+
+    learning_rate_multiplier: Union[Literal["auto"], float]
+    """Scaling factor for the learning rate.
+
+    A smaller learning rate may be useful to avoid overfitting.
+    """
+
+    n_epochs: Union[Literal["auto"], int]
+    """The number of epochs to train the model for.
+
+    An epoch refers to one full cycle through the training dataset.
+    """
+
+
+class MethodSupervised(TypedDict, total=False):
+    hyperparameters: MethodSupervisedHyperparameters
+    """The hyperparameters used for the fine-tuning job."""
+
+
+class Method(TypedDict, total=False):
+    dpo: MethodDpo
+    """Configuration for the DPO fine-tuning method."""
+
+    supervised: MethodSupervised
+    """Configuration for the supervised fine-tuning method."""
+
+    type: Literal["supervised", "dpo"]
+    """The type of method. Is either `supervised` or `dpo`."""
diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/job_list_events_params.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/job_list_events_params.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1c9a64dc85b994ac92deb3fa5fa216c13bd16c4
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/job_list_events_params.py
@@ -0,0 +1,15 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing_extensions import TypedDict
+
+__all__ = ["JobListEventsParams"]
+
+
+class JobListEventsParams(TypedDict, total=False):
+    after: str
+    """Identifier for the last event from the previous pagination request."""
+
+    limit: int
+    """Number of events to retrieve."""
diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/job_list_params.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/job_list_params.py
new file mode 100644
index 0000000000000000000000000000000000000000..5c075ca33f0a1446d21bca9783ac6c66d71dd7c9
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/job_list_params.py
@@ -0,0 +1,15 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing_extensions import TypedDict
+
+__all__ = ["JobListParams"]
+
+
+class JobListParams(TypedDict, total=False):
+    after: str
+    """Identifier for the last job from the previous pagination request."""
+
+    limit: int
+    """Number of fine-tuning jobs to retrieve."""
diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/__init__.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c93da1b69f90552c97167f86cd345fd8dc05867
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/__init__.py
@@ -0,0 +1,6 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from .checkpoint_list_params import CheckpointListParams as CheckpointListParams
+from .fine_tuning_job_checkpoint import FineTuningJobCheckpoint as FineTuningJobCheckpoint
diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/__pycache__/__init__.cpython-311.pyc b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/__pycache__/__init__.cpython-311.pyc
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diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/__pycache__/fine_tuning_job_checkpoint.cpython-311.pyc b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/__pycache__/fine_tuning_job_checkpoint.cpython-311.pyc
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diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/checkpoint_list_params.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/checkpoint_list_params.py
new file mode 100644
index 0000000000000000000000000000000000000000..adceb3b218c8c2cf5eb6c6ce8b975766e5a93c7a
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/checkpoint_list_params.py
@@ -0,0 +1,15 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from __future__ import annotations
+
+from typing_extensions import TypedDict
+
+__all__ = ["CheckpointListParams"]
+
+
+class CheckpointListParams(TypedDict, total=False):
+    after: str
+    """Identifier for the last checkpoint ID from the previous pagination request."""
+
+    limit: int
+    """Number of checkpoints to retrieve."""
diff --git a/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/fine_tuning_job_checkpoint.py b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/fine_tuning_job_checkpoint.py
new file mode 100644
index 0000000000000000000000000000000000000000..bd07317a3e94bff0f2943504d6058da9026d1588
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/fine_tuning/jobs/fine_tuning_job_checkpoint.py
@@ -0,0 +1,47 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import Optional
+from typing_extensions import Literal
+
+from ...._models import BaseModel
+
+__all__ = ["FineTuningJobCheckpoint", "Metrics"]
+
+
+class Metrics(BaseModel):
+    full_valid_loss: Optional[float] = None
+
+    full_valid_mean_token_accuracy: Optional[float] = None
+
+    step: Optional[float] = None
+
+    train_loss: Optional[float] = None
+
+    train_mean_token_accuracy: Optional[float] = None
+
+    valid_loss: Optional[float] = None
+
+    valid_mean_token_accuracy: Optional[float] = None
+
+
+class FineTuningJobCheckpoint(BaseModel):
+    id: str
+    """The checkpoint identifier, which can be referenced in the API endpoints."""
+
+    created_at: int
+    """The Unix timestamp (in seconds) for when the checkpoint was created."""
+
+    fine_tuned_model_checkpoint: str
+    """The name of the fine-tuned checkpoint model that is created."""
+
+    fine_tuning_job_id: str
+    """The name of the fine-tuning job that this checkpoint was created from."""
+
+    metrics: Metrics
+    """Metrics at the step number during the fine-tuning job."""
+
+    object: Literal["fine_tuning.job.checkpoint"]
+    """The object type, which is always "fine_tuning.job.checkpoint"."""
+
+    step_number: int
+    """The step number that the checkpoint was created at."""
diff --git a/.venv/lib/python3.11/site-packages/openai/types/shared/function_parameters.py b/.venv/lib/python3.11/site-packages/openai/types/shared/function_parameters.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3d83e3496f94ba1ff63ca2b1088ac48a64d07f2
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/shared/function_parameters.py
@@ -0,0 +1,8 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import Dict
+from typing_extensions import TypeAlias
+
+__all__ = ["FunctionParameters"]
+
+FunctionParameters: TypeAlias = Dict[str, object]
diff --git a/.venv/lib/python3.11/site-packages/openai/types/shared/metadata.py b/.venv/lib/python3.11/site-packages/openai/types/shared/metadata.py
new file mode 100644
index 0000000000000000000000000000000000000000..0da88c679c27f27642b9162a17780c8930cc3bdc
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/shared/metadata.py
@@ -0,0 +1,8 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import Dict
+from typing_extensions import TypeAlias
+
+__all__ = ["Metadata"]
+
+Metadata: TypeAlias = Dict[str, str]
diff --git a/.venv/lib/python3.11/site-packages/openai/types/shared/response_format_json_object.py b/.venv/lib/python3.11/site-packages/openai/types/shared/response_format_json_object.py
new file mode 100644
index 0000000000000000000000000000000000000000..107728dd2e7073645101d9cfb5b6d283825a8054
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/shared/response_format_json_object.py
@@ -0,0 +1,12 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing_extensions import Literal
+
+from ..._models import BaseModel
+
+__all__ = ["ResponseFormatJSONObject"]
+
+
+class ResponseFormatJSONObject(BaseModel):
+    type: Literal["json_object"]
+    """The type of response format being defined: `json_object`"""
diff --git a/.venv/lib/python3.11/site-packages/openai/types/shared/response_format_json_schema.py b/.venv/lib/python3.11/site-packages/openai/types/shared/response_format_json_schema.py
new file mode 100644
index 0000000000000000000000000000000000000000..3194a4fe9132abc6e04de7d037ed0e90e5031f86
--- /dev/null
+++ b/.venv/lib/python3.11/site-packages/openai/types/shared/response_format_json_schema.py
@@ -0,0 +1,44 @@
+# File generated from our OpenAPI spec by Stainless. See CONTRIBUTING.md for details.
+
+from typing import Dict, Optional
+from typing_extensions import Literal
+
+from pydantic import Field as FieldInfo
+
+from ..._models import BaseModel
+
+__all__ = ["ResponseFormatJSONSchema", "JSONSchema"]
+
+
+class JSONSchema(BaseModel):
+    name: str
+    """The name of the response format.
+
+    Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length
+    of 64.
+    """
+
+    description: Optional[str] = None
+    """
+    A description of what the response format is for, used by the model to determine
+    how to respond in the format.
+    """
+
+    schema_: Optional[Dict[str, object]] = FieldInfo(alias="schema", default=None)
+    """The schema for the response format, described as a JSON Schema object."""
+
+    strict: Optional[bool] = None
+    """Whether to enable strict schema adherence when generating the output.
+
+    If set to true, the model will always follow the exact schema defined in the
+    `schema` field. Only a subset of JSON Schema is supported when `strict` is
+    `true`. To learn more, read the
+    [Structured Outputs guide](https://platform.openai.com/docs/guides/structured-outputs).
+    """
+
+
+class ResponseFormatJSONSchema(BaseModel):
+    json_schema: JSONSchema
+
+    type: Literal["json_schema"]
+    """The type of response format being defined: `json_schema`"""