Uses of Class
ai.z.openapi.service.fine_turning.FineTuningJob

Packages that use FineTuningJob

Package	Description
ai.z.openapi.api.fine_tuning

Uses of FineTuningJob in ai.z.openapi.api.fine_tuning

Methods in ai.z.openapi.api.fine_tuning that return types with arguments of type FineTuningJob

Modifier and Type	Method and Description
io.reactivex.Single<FineTuningJob>	FineTuningApi.cancelFineTuningJob(String fineTuningJobId) Cancel a running fine-tuning job Stops the training process and marks the job as cancelled
io.reactivex.Single<FineTuningJob>	FineTuningApi.createFineTuningJob(FineTuningJobRequest request) Create a new fine-tuning job for model customization Initiates the training process for customizing GLM-4, CodeGeeX-4, or other base models Supports various fine-tuning strategies including LoRA, full parameter tuning, and adapter methods Enables domain adaptation for specific use cases like coding, conversation, or specialized knowledge
io.reactivex.Single<FineTuningJob>	FineTuningApi.deleteFineTuningJob(String fineTuningJobId) Delete a fine-tuning job Permanently removes the fine-tuning job and associated metadata
io.reactivex.Single<FineTuningJob>	FineTuningApi.retrieveFineTuningJob(String fineTuningJobId, Integer limit, String after) Retrieve comprehensive details of a specific fine-tuning job Gets complete information about job status, training progress, model performance, and configuration Includes training metrics, validation results, estimated completion time, and resource usage Provides insights into model convergence and fine-tuning effectiveness