Fine-Tuned Models

Documentation Index

Fetch the complete documentation index at: https://koreai.mintlify.app/llms.txt

Use this file to discover all available pages before exploring further.

Fine-tuned models let you customize base models for your specific use cases by training them on your own datasets. You can fine-tune Platform-hosted models or import base models from Hugging Face, then deploy them for inference within the Platform or externally via API.

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View Models & Deployments

• Models List: Go to Models > Fine-tuned models to see all fine-tuned models with their deployment status. Click a model to open its detail view with Overview, Deployments, and Configurations tabs.
• Deployments List: Each model can have multiple independent deployments. The Deployments tab shows the name, id, status and other attributes of the models.

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Create a Fine-Tuned Model

Go to Models > Fine-tuned models > Start fine-tuning and complete the following steps:

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Step 1: General Details

Enter model name, description, and tags for searchability.

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Step 2: Select Base Model

Choose your base model source: Platform-hosted: Select from the dropdown list (includes previously imported models). Import from Hugging Face: Select your Hugging Face connection and enter the model name. See Enable Hugging Face for connection setup.

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Step 3: Fine-Tuning Configuration

Configure training parameters: Fine-tuning type by model size:

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Step 4: Training & Evaluation Datasets

Training Dataset: Select or upload the dataset to train the model. Accepts JSONL, CSV, or JSON files with at least two columns: prompt and completion. Evaluation Dataset (choose one):

• Use from training dataset: Allocates a percentage (default 15%) for evaluation
• Upload evaluation dataset: Use a separate dataset
• Skip evaluation: Skip the evaluation step

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Step 5: Test Dataset (Optional)

Upload a test dataset to evaluate the fine-tuned model after training completes.

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Step 6: Hardware Selection

Select the hardware configuration for fine-tuning from the available options.

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Step 7: Weights & Biases Integration (Optional)

Connect your W&B account to monitor fine-tuning metrics in real-time. Select an existing connection or create a new one. See Integrate with Weights & Biases.

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Step 8: Review & Start

Review all settings and click Start fine-tuning. The Overview page displays real-time progress.

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Training Overview

The Overview page displays real-time fine-tuning progress: General Information: Progress status (Initializing, Training in progress, Testing in progress, Fine-tuning completed, Stopped, Failed), total time, and author. Base Model Information: Source model and origin. Training Information: Training type, steps, training loss, validation percentage, validation loss, start time, and duration. Click the arrows next to loss fields to view graphical trends. Test Data Information: Model performance measured by BLEU score. Hardware Information: CPU and GPU utilization during fine-tuning. Training Parameters: Summary of configured parameters. Status handling:

• If fine-tuning fails, view the reason and click Re-trigger to restart
• If stopped manually, click Re-trigger to restart from the beginning

After completion, download training files, test results, and test data for reference.

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Deploy a Fine-Tuned Model

Once fine-tuning completes, deploy the model for inference.

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Deployment Steps

1. Go to the model’s Overview or Model Endpoint page and click Deploy model
2. Enter deployment name, description, and tags
3. Configure parameters (see below)
4. Select hardware for deployment
5. Review, accept terms, and click Deploy

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Deployment Parameters

Inference Parameters: Scaling Parameters: After deployment completes, the status changes to “Deployed” and an API endpoint is generated.

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Manage Deployed Models

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Model Endpoint

After deployment, the API endpoint enables external inferencing. Access via the Model Endpoint tab. The endpoint is available in three formats: cURL, Python, and Node.js. Copy the appropriate format for your integration. Platform usage: Use the deployed model in Prompt Playground or AI Nodes in tool flows.

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Deployment History

The deployment history tracks all versions of the model: Version naming: The system auto-increments version numbers. First deployment: ModelName_v1, subsequent: ModelName_v2, ModelName_v3, etc. The name persists even if edited. The most recent deployment is marked with a green tick.

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API Keys

Generate API keys for secure external access. Keys are scoped per deployment.

1. Go to API Keys tab
2. Click Create a new API key
3. Enter a name and click Generate key
4. Copy the key immediately—it won’t be shown again

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Configurations

Model Endpoint Timeout: Set timeout duration from 30-180 seconds (default: 60 seconds). Timeout precedence: Tool > Node > Model. Undeploy: Disconnects the model from all active instances immediately. Click Proceed to undeploy. Delete: Removes the model and all associated data. Only available for undeployed models. Click Proceed to delete.

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Re-deploy a Model

To update deployment parameters or hardware:

1. Go to the deployed model’s Model Endpoint page
2. Click Deploy model
3. Modify parameters as needed
4. Complete the deployment wizard

The system creates a new version in the deployment history.

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Export a Model

Export fine-tuned models for backup or reference:

1. On the Models page, click the three-dot menu next to the model name
2. Select Export model
3. The ZIP file downloads to your local machine

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Iterative Fine-Tuning

You can fine-tune on top of an existing fine-tuned model:

1. When selecting a base model, choose a previously fine-tuned model from the Platform-hosted dropdown
2. Continue with the standard fine-tuning process

This enables iterative improvement of your models.