> ## Documentation Index > Fetch the complete documentation index at: https://docs.maia.ai/llms.txt > Use this file to discover all available pages before exploring further. # Cortex Finetune export const ComponentMetadata = ({warehouses, unsupportedWarehouses = [], componentType, connectionInputs, connectionOutputs}) => { const allWarehouses = [...warehouses.map(w => ({ name: w, supported: true })), ...unsupportedWarehouses.map(w => ({ name: w, supported: false }))]; return

Project Availability	{allWarehouses.map((w, i) => {w.name} {w.supported ? '✅' : '❌'} )}
Component Type	{componentType}
Connection Inputs	{connectionInputs}
Connection Outputs	{connectionOutputs}

; }; Production use of this feature is available for specific editions only. [Contact our sales team](https://www.matillion.com/contact) for more information. The [Cortex Finetune](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-finetuning) orchestration component lets you fine-tune large language models (LLMs) using Snowflake Cortex. With this component, you can adapt powerful pre-trained LLMs to your organization's specific use case—whether it's customer support, summarization, content generation, or domain-specific reasoning—without needing to train models from scratch. Fine-tuning a model on your own labeled dataset allows for more accurate, reliable responses aligned with your data and requirements. The resulting model can be called using your assigned name via the `CORTEX.COMPLETE` function in Snowflake. To use this component, you must use a Snowflake role that has been granted the [SNOWFLAKE.CORTEX\_USER database role](https://docs.snowflake.com/en/sql-reference/snowflake-db-roles#label-snowflake-db-roles-cortex-schema). Read [Required Privileges](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions#label-cortex-llm-privileges) to learn more about granting this privilege. To learn more about Snowflake Cortex, such as availability, usage quotas, managing costs, and more, read [Large Language Model (LLM) Functions (Snowflake Cortex)](https://docs.snowflake.com/en/user-guide/snowflake-cortex/llm-functions). ### Use case The versatility of this component means it can be used in many ways. For example, you can use it to: * Fine-tune a model using technical support transcripts to generate responses to new support tickets. * Fine-tune a model using internal financial data and report templates to automatically draft summaries and analysis documents. *** ## Properties Reference material is provided below for the Model, Training Data, and Validation Data properties. A human-readable name for the component. ### Model This name is used to reference the fine-tuned model in downstream Cortex functions. {/* */} Select the base LLM that will be fine-tuned using your training data. Available models include: * **llama3-8b:** Optimized for text classification, summarization, and sentiment analysis. * **llama3-70b:** High-performance model ideal for chat, content creation, and enterprise use. * **llama3.1-8b:** Lightweight, fast model with a 24K context window for moderate tasks. * **llama3.1-70b:** Cost-effective, open-source model for advanced enterprise applications. * **mistral-7b:** Fast, efficient model for summarization and simple question answering. * **mistral-8x7b:** Versatile model for generation, classification, and QA with low latency. Choosing a smaller model (such as `llama3-8b`) is recommended for faster training and cost-efficiency in smaller-scale or experimental use cases. {/* */} Select the Snowflake database where your training and (optionally) validation tables are stored. {/* */} Select the schema within the chosen database that contains your input tables. {/* */} Defines how the component executes the model creation process. * **Synchronous:** The component runs in a blocking manner and waits for the training job to complete. * **Asynchronous:** Initiates the training job and allows the pipeline to continue running other components without waiting for completion. - For asynchronous mode, you can find the job ID in the task history table to track job progress. - For synchronous mode, the job ID, job status, and model name are recorded in the pipeline logs. ### Training Data You must supply a labeled dataset that contains pairs of prompts and expected completions. Select the table that contains your training data. {/* */} Choose the column containing the user-provided prompts. {/* */} Choose the column containing the expected or target responses for those prompts. ### Validation Data To assess model performance, you can either auto-split the training data or provide a separate validation dataset. * **Yes:** A portion of the training table is automatically used as validation data. You do not need to provide a separate validation table. * **No:** You must supply a separate validation table and define corresponding columns. {/* */} Select the table that contains your validation data. Only available if `Automatically Split Training Data` is set to `No`. {/* */} Select the prompt column in the validation dataset. Only available if `Automatically Split Training Data` is set to `No`. {/* */} Select the column with expected completions in the validation dataset. Only available if `Automatically Split Training Data` is set to `No`. {/* */} Specify the number of epochs—that is, the number of times the model should pass through the full training dataset during fine-tuning. A higher number of epochs may improve accuracy but may also increase training time and cost. Once fine-tuning completes successfully, the model will be available for use with the assigned name through the `CORTEX.COMPLETE` function in Snowflake SQL and available via {maia}'s [Cortex Completions](/docs/components/cortex-completions) component. The model is registered within your Snowflake account under the selected database and schema.