Initial commit: KidsAI Explorer with complete functionality

- Complete KidsAI Explorer application - Multi-language support (English/German) - AI-powered educational guidance using OpenAI - Interactive chat interface for children - Proper placeholder translation fixes - Mobile-responsive design - Educational framework for critical thinking
2025-07-13 16:59:42 +02:00
commit 500bd192d5
2450 changed files with 202563 additions and 0 deletions
--- a/node_modules/openai/resources/fine-tuning/methods.d.mts
+++ b/node_modules/openai/resources/fine-tuning/methods.d.mts
@@ -0,0 +1,120 @@
+import { APIResource } from "../../core/resource.mjs";
+import * as GraderModelsAPI from "../graders/grader-models.mjs";
+export declare class Methods extends APIResource {
+}
+/**
+ * The hyperparameters used for the DPO fine-tuning job.
+ */
+export interface DpoHyperparameters {
+    /**
+     * Number of examples in each batch. A larger batch size means that model
+     * parameters are updated less frequently, but with lower variance.
+     */
+    batch_size?: 'auto' | number;
+    /**
+     * The beta value for the DPO method. A higher beta value will increase the weight
+     * of the penalty between the policy and reference model.
+     */
+    beta?: 'auto' | number;
+    /**
+     * Scaling factor for the learning rate. A smaller learning rate may be useful to
+     * avoid overfitting.
+     */
+    learning_rate_multiplier?: 'auto' | number;
+    /**
+     * The number of epochs to train the model for. An epoch refers to one full cycle
+     * through the training dataset.
+     */
+    n_epochs?: 'auto' | number;
+}
+/**
+ * Configuration for the DPO fine-tuning method.
+ */
+export interface DpoMethod {
+    /**
+     * The hyperparameters used for the DPO fine-tuning job.
+     */
+    hyperparameters?: DpoHyperparameters;
+}
+/**
+ * The hyperparameters used for the reinforcement fine-tuning job.
+ */
+export interface ReinforcementHyperparameters {
+    /**
+     * Number of examples in each batch. A larger batch size means that model
+     * parameters are updated less frequently, but with lower variance.
+     */
+    batch_size?: 'auto' | number;
+    /**
+     * Multiplier on amount of compute used for exploring search space during training.
+     */
+    compute_multiplier?: 'auto' | number;
+    /**
+     * The number of training steps between evaluation runs.
+     */
+    eval_interval?: 'auto' | number;
+    /**
+     * Number of evaluation samples to generate per training step.
+     */
+    eval_samples?: 'auto' | number;
+    /**
+     * Scaling factor for the learning rate. A smaller learning rate may be useful to
+     * avoid overfitting.
+     */
+    learning_rate_multiplier?: 'auto' | number;
+    /**
+     * The number of epochs to train the model for. An epoch refers to one full cycle
+     * through the training dataset.
+     */
+    n_epochs?: 'auto' | number;
+    /**
+     * Level of reasoning effort.
+     */
+    reasoning_effort?: 'default' | 'low' | 'medium' | 'high';
+}
+/**
+ * Configuration for the reinforcement fine-tuning method.
+ */
+export interface ReinforcementMethod {
+    /**
+     * The grader used for the fine-tuning job.
+     */
+    grader: GraderModelsAPI.StringCheckGrader | GraderModelsAPI.TextSimilarityGrader | GraderModelsAPI.PythonGrader | GraderModelsAPI.ScoreModelGrader | GraderModelsAPI.MultiGrader;
+    /**
+     * The hyperparameters used for the reinforcement fine-tuning job.
+     */
+    hyperparameters?: ReinforcementHyperparameters;
+}
+/**
+ * The hyperparameters used for the fine-tuning job.
+ */
+export interface SupervisedHyperparameters {
+    /**
+     * Number of examples in each batch. A larger batch size means that model
+     * parameters are updated less frequently, but with lower variance.
+     */
+    batch_size?: 'auto' | number;
+    /**
+     * Scaling factor for the learning rate. A smaller learning rate may be useful to
+     * avoid overfitting.
+     */
+    learning_rate_multiplier?: 'auto' | number;
+    /**
+     * The number of epochs to train the model for. An epoch refers to one full cycle
+     * through the training dataset.
+     */
+    n_epochs?: 'auto' | number;
+}
+/**
+ * Configuration for the supervised fine-tuning method.
+ */
+export interface SupervisedMethod {
+    /**
+     * The hyperparameters used for the fine-tuning job.
+     */
+    hyperparameters?: SupervisedHyperparameters;
+}
+export declare namespace Methods {
+    export { type DpoHyperparameters as DpoHyperparameters, type DpoMethod as DpoMethod, type ReinforcementHyperparameters as ReinforcementHyperparameters, type ReinforcementMethod as ReinforcementMethod, type SupervisedHyperparameters as SupervisedHyperparameters, type SupervisedMethod as SupervisedMethod, };
+}
+//# sourceMappingURL=methods.d.mts.map