pub struct CreateFineTuningJobRequestArgs { /* private fields */ }
Expand description
Builder for CreateFineTuningJobRequest
.
Implementations§
source§impl CreateFineTuningJobRequestArgs
impl CreateFineTuningJobRequestArgs
sourcepub fn model<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self
pub fn model<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self
The name of the model to fine-tune. You can select one of the supported models.
sourcepub fn training_file<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self
pub fn training_file<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self
The ID of an uploaded file that contains training data.
See upload file 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 or completions format.
See the fine-tuning guide for more details.
sourcepub fn hyperparameters<VALUE: Into<Hyperparameters>>(
&mut self,
value: VALUE,
) -> &mut Self
pub fn hyperparameters<VALUE: Into<Hyperparameters>>( &mut self, value: VALUE, ) -> &mut Self
The hyperparameters used for the fine-tuning job.
sourcepub fn suffix<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self
pub fn suffix<VALUE: Into<String>>(&mut self, value: VALUE) -> &mut Self
A string of up to 18 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
.
sourcepub fn validation_file<VALUE: Into<String>>(
&mut self,
value: VALUE,
) -> &mut Self
pub fn validation_file<VALUE: Into<String>>( &mut self, value: VALUE, ) -> &mut Self
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 for more details.
sourcepub fn integrations<VALUE: Into<Vec<FineTuningIntegration>>>(
&mut self,
value: VALUE,
) -> &mut Self
pub fn integrations<VALUE: Into<Vec<FineTuningIntegration>>>( &mut self, value: VALUE, ) -> &mut Self
A list of integrations to enable for your fine-tuning job.
sourcepub fn seed<VALUE: Into<u32>>(&mut self, value: VALUE) -> &mut Self
pub fn seed<VALUE: Into<u32>>(&mut self, value: VALUE) -> &mut Self
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.
sourcepub fn build(&self) -> Result<CreateFineTuningJobRequest, OpenAIError>
pub fn build(&self) -> Result<CreateFineTuningJobRequest, OpenAIError>
Trait Implementations§
source§impl Clone for CreateFineTuningJobRequestArgs
impl Clone for CreateFineTuningJobRequestArgs
source§fn clone(&self) -> CreateFineTuningJobRequestArgs
fn clone(&self) -> CreateFineTuningJobRequestArgs
1.6.0 · source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
source
. Read moreAuto Trait Implementations§
impl Freeze for CreateFineTuningJobRequestArgs
impl RefUnwindSafe for CreateFineTuningJobRequestArgs
impl Send for CreateFineTuningJobRequestArgs
impl Sync for CreateFineTuningJobRequestArgs
impl Unpin for CreateFineTuningJobRequestArgs
impl UnwindSafe for CreateFineTuningJobRequestArgs
Blanket Implementations§
source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
source§impl<T> CloneToUninit for Twhere
T: Clone,
impl<T> CloneToUninit for Twhere
T: Clone,
source§unsafe fn clone_to_uninit(&self, dst: *mut T)
unsafe fn clone_to_uninit(&self, dst: *mut T)
clone_to_uninit
)