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Commit 9a05c6a

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TheoKanningTheoKanning
authored
Add file and fine-tune api support (TheoKanning#6)
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‎api/src/main/java/com/theokanning/openai/DeleteResult.java‎

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package com.theokanning.openai;
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import lombok.Data;
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/**
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* A response when deleting an object
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*/
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@Data
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public class DeleteResult {
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/**
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* The id of the object.
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*/
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String id;
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/**
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* The type of object deleted, for example "file" or "model"
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*/
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String object;
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/**
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* True if successfully deleted
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*/
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boolean deleted;
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}
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‎api/src/main/java/com/theokanning/openai/completion/CompletionRequest.java‎

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@Data
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public class CompletionRequest {
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/**
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* The name of the model to use, only used if specifying a fine tuned model.
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*/
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String model;
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/**
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* An optional prompt to complete from
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*/
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‎api/src/main/java/com/theokanning/openai/completion/CompletionResult.java‎

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String object;
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/**
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* The creation time in epoch milliseconds.
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* The creation time in epoch seconds.
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*/
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long created;
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‎api/src/main/java/com/theokanning/openai/file/File.java‎

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package com.theokanning.openai.file;
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import lombok.Data;
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/**
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* A file uploaded to OpenAi
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*
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* https://beta.openai.com/docs/api-reference/files
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*/
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@Data
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public class File {
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/**
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* The unique id of this file.
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*/
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String id;
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/**
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* The type of object returned, should be "file".
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*/
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String object;
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/**
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* File size in bytes.
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*/
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Long bytes;
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/**
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* The creation time in epoch seconds.
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*/
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Long createdAt;
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/**
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* The name of the file.
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*/
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String filename;
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/**
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* Description of the file's purpose.
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*/
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String purpose;
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}
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‎api/src/main/java/com/theokanning/openai/finetune/FineTuneEvent.java‎

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package com.theokanning.openai.finetune;
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import lombok.Data;
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/**
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* An object representing an event in the lifecycle of a fine-tuning job
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*
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* https://beta.openai.com/docs/api-reference/fine-tunes
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*/
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@Data
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public class FineTuneEvent {
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/**
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* The type of object returned, should be "fine-tune-event".
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*/
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String object;
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/**
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* The creation time in epoch seconds.
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*/
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Long createdAt;
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/**
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* The log level of this message.
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*/
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String level;
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/**
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* The event message.
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*/
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String message;
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}
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‎api/src/main/java/com/theokanning/openai/finetune/FineTuneRequest.java‎

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package com.theokanning.openai.finetune;
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import lombok.AllArgsConstructor;
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import lombok.Builder;
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import lombok.Data;
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import lombok.NoArgsConstructor;
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import java.util.List;
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/**
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* A request for OpenAi to create a fine-tuned model
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* All fields except trainingFile are nullable.
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*
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* https://beta.openai.com/docs/api-reference/fine-tunes/create
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*/
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@Builder
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@NoArgsConstructor
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@AllArgsConstructor
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@Data
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public class FineTuneRequest {
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/**
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* The ID of an uploaded file that contains training data.
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*/
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String trainingFile;
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/**
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* The ID of an uploaded file that contains validation data.
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*/
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String validationFile;
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/**
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* The name of the base model to fine-tune. You can select one of "ada", "babbage", "curie", or "davinci".
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* To learn more about these models, see the Engines documentation.
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*/
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String model;
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/**
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* The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.
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*/
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Integer nEpochs;
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/**
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* The batch size to use for training.
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* The batch size is the number of training examples used to train a single forward and backward pass.
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*
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* By default, the batch size will be dynamically configured to be ~0.2% of the number of examples in the training
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* set, capped at 256 - in general, we've found that larger batch sizes tend to work better for larger datasets.
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*/
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Integer batchSize;
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/**
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* The learning rate multiplier to use for training.
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* The fine-tuning learning rate is the original learning rate used for pretraining multiplied by this value.
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*
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* By default, the learning rate multiplier is the 0.05, 0.1, or 0.2 depending on final batch_size
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* (larger learning rates tend to perform better with larger batch sizes).
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* We recommend experimenting with values in the range 0.02 to 0.2 to see what produces the best results.
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*/
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Double learningRateMultiplier;
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/**
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* The weight to use for loss on the prompt tokens.
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* This controls how much the model tries to learn to generate the prompt
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* (as compared to the completion which always has a weight of 1.0),
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* and can add a stabilizing effect to training when completions are short.
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*
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* If prompts are extremely long (relative to completions), it may make sense to reduce this weight so as to
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* avoid over-prioritizing learning the prompt.
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*/
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Double promptLossWeight;
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/**
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* If set, we calculate classification-specific metrics such as accuracy and F-1 score using the validation set
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* at the end of every epoch. These metrics can be viewed in the results file.
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*
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* In order to compute classification metrics, you must provide a validation_file.
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* Additionally, you must specify {@link FineTuneRequest#classificationNClasses} for multiclass
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* classification or {@link FineTuneRequest#classificationPositiveClass} for binary classification.
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*/
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Boolean computeClassificationMetrics;
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/**
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* The number of classes in a classification task.
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*
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* This parameter is required for multiclass classification.
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*/
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Integer classificationNClasses; // todo verify snake case
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/**
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* The positive class in binary classification.
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*
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* This parameter is needed to generate precision, recall, and F1 metrics when doing binary classification.
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*/
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String classificationPositiveClass;
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/**
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* If this is provided, we calculate F-beta scores at the specified beta values.
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* The F-beta score is a generalization of F-1 score. This is only used for binary classification.
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*
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* With a beta of 1 (i.e. the F-1 score), precision and recall are given the same weight.
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* A larger beta score puts more weight on recall and less on precision.
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* A smaller beta score puts more weight on precision and less on recall.
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*/
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List<Double> classificationBetas;
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/**
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* A string of up to 40 characters that will be added to your fine-tuned model name.
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*/
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String suffix;
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}
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‎api/src/main/java/com/theokanning/openai/finetune/FineTuneResult.java‎

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package com.theokanning.openai.finetune;
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import com.theokanning.openai.file.File;
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import lombok.Data;
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import java.util.List;
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/**
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* An object describing an fine-tuned model. Returned by multiple fine-tune requests.
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*
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* https://beta.openai.com/docs/api-reference/fine-tunes
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*/
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@Data
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public class FineTuneResult {
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/**
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* The ID of the fine-tuning job.
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*/
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String id;
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/**
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* The type of object returned, should be "fine-tune".
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*/
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String object;
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/**
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* The name of the base model.
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*/
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String model;
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/**
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* The creation time in epoch seconds.
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*/
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Long createdAt;
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/**
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* List of events in this job's lifecycle. Null when getting a list of fine-tune jobs.
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*/
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List<FineTuneEvent> events;
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/**
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* The ID of the fine-tuned model, null if tuning job is not finished.
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* This is the id used to call the model.
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*/
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String fineTunedModel;
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/**
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* The specified hyper-parameters for the tuning job.
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*/
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HyperParameters hyperparams;
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/**
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* The ID of the organization this model belongs to.
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*/
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String organizationId;
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/**
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* Result files for this fine-tune job.
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*/
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List<File> resultFiles;
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/**
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* The status os the fine-tune job. "pending", "succeeded", or "cancelled"
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*/
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String status;
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/**
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* Training files for this fine-tune job.
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*/
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List<File> trainingFiles;
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/**
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* The last update time in epoch seconds.
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*/
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Long updatedAt;
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/**
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* Validation files for this fine-tune job.
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*/
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List<File> validationFiles;
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}
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‎api/src/main/java/com/theokanning/openai/finetune/HyperParameters.java‎

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package com.theokanning.openai.finetune;
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import lombok.Data;
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/**
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* Fine-tuning job hyperparameters
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*
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* https://beta.openai.com/docs/api-reference/fine-tunes
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*/
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@Data
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public class HyperParameters {
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/**
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* The batch size to use for training.
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*/
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String batchSize;
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/**
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* The learning rate multiplier to use for training.
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*/
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Double learningRateMultiplier;
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/**
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* The number of epochs to train the model for.
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*/
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Integer nEpochs;
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/**
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* The weight to use for loss on the prompt tokens.
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*/
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Double promptLossWeight;
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}
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‎client/build.gradle‎

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api 'com.squareup.retrofit2:retrofit:2.9.0'
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implementation 'com.squareup.retrofit2:adapter-rxjava2:2.9.0'
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implementation 'com.squareup.retrofit2:converter-jackson:2.9.0'
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testImplementation(platform('org.junit:junit-bom:5.8.2'))
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testImplementation('org.junit.jupiter:junit-jupiter')
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}
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test {
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useJUnitPlatform()
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testLogging {
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events "passed", "skipped", "failed"
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}
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}
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ext {

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