Fine Tune Your AI Models with Ease

Fine tuning your AI models can be a daunting task, but it doesn't have to be. In fact, it's a crucial step in getting the most out of your AI. By fine tuning, you can improve the accuracy and performance of your models.

One way to fine tune your AI models is by using transfer learning. This technique allows you to take a pre-trained model and adapt it to your specific task or dataset. As we learned in our article, transfer learning can save you a significant amount of time and resources.

Fine tuning can also be done using techniques like data augmentation and hyperparameter tuning. Data augmentation involves artificially increasing the size of your dataset by applying transformations to the existing data. This can help improve the robustness of your model.

Fine-tuning is a technique in deep learning that involves taking a pre-trained model and making minor adjustments to its internal parameters.

It's a form of transfer learning that allows us to leverage the valuable features and representations learned by the model from a vast dataset.

Typically, the overall architecture of the pre-trained model remains mostly intact during the fine-tuning process.

Fine-tuning is particularly useful when we don't have a lot of labeled data for a specific task, as it can help us effectively train models even with limited data.

By starting with a pre-trained model, we can skip the initial stages of training and focus on adapting the model to the specific task at hand.

Fine-tuning a pre-trained model can significantly reduce the time and resources required to achieve good results, making it a popular technique in the field of machine learning.

Preparing Data is a crucial step in fine-tuning your Large Language Model (LLM). You'll need to create a dataset that includes prompt and completion pairs, which are the input and response from the LLM.

Your training data must come from a Snowflake table or view, and the query result must contain columns named prompt and completion. If your table or view doesn't have these column names, use a column alias in your query to rename them.

A good starting point is to begin with a few hundred examples. Starting with too many examples can increase tuning time drastically with minimal improvement in performance.

To ensure optimal performance, keep in mind that the portion of the context window allocated for prompt and completion for each base model is defined in the following table:

Remember, all columns other than the prompt and completion columns will be ignored by the FINETUNE function, so it's best to use a query that selects only the columns you need.

To fine-tune a model, you need to prepare the training data and start the fine-tuning job with the required parameters. Once training is complete, you can use the model name provided by Cortex Fine-tuning to run inference on your model.

To adjust the architecture of a pre-trained model, you may need to modify the top layers to fit the requirements of your specific task. This typically involves changing the number of output neurons in the final layer to match the number of classes in your classification task.

You can choose to freeze some layers in the pre-trained model, which means preventing them from updating their weights during the fine-tuning process. This can be beneficial if the lower layers of the pre-trained model have already learned general features that are useful for your task.

It's advisable to use a smaller learning rate than what was used in the initial pre-training phase during training. This helps prevent drastic changes to the already learned representations while allowing the model to adapt to the new data.

Here are some general guidelines for fine-tuning a model:

By following these steps and guidelines, you can fine-tune a model effectively and achieve impressive results in your machine learning endeavors.

To model, you need to prepare the training data. This involves selecting a pre-trained model that matches the nature of your task, such as a pre-trained image classification model for an image classification task.

The first step in fine-tuning a model is to choose a pre-trained model that matches the nature of your task. For example, if you are working on an image classification task, you can start with a pre-trained image classification model. It’s essential to select a model with similar or related features to the task you want to tackle.

You can start a fine-tuning job by calling the SNOWFLAKE.CORTEX.FINETUNE function and passing in ‘CREATE’ as the first argument, or using Snowsight.

To fine-tune a model, you need to make modifications to the model’s architecture to fit the requirements of your specific task. This typically involves modifying the top layers of the model. For example, you may need to change the number of output neurons in the final layer to match the number of classes in your classification task.

Here are the steps to fine-tune a model:

Fine-tuning modifies an already trained model for specific improvements, whereas training from scratch builds a model’s abilities from the ground up. Fine-tuning is typically faster and less resource-intensive due to leveraging pre-learned patterns.

To fine-tune a model, you can use a smaller learning rate than what was used in the initial pre-training phase. This helps prevent drastic changes to the already learned representations while allowing the model to adapt to the new data.

Fine-tuning a model is generally quicker than full model training because it starts with a pre-trained base. This is a significant advantage, especially when working with large datasets.

The duration of fine-tuning can vary based on the size of the dataset, but it's often faster than full model training.

Fine-tuning incurs compute cost based on the number of tokens used in training, and running the COMPLETE function on a fine-tuned model incurs compute costs based on the number of tokens processed. Refer to the Snowflake Service Consumption Table for each cost in credits per million tokens.

A token is approximately equal to four characters of text, and the equivalence of raw input or output text to tokens can vary by model. For the COMPLETE function, both input and output tokens are counted.

The number of fine-tuning trained tokens is calculated as follows: Fine-tuning trained tokens = number of input tokens * number of epochs trained. You can use the FINETUNE ('DESCRIBE') (SNOWFLAKE.CORTEX) to see the number of trained tokens for your fine-tuning job.

Here are the limits for training/validation dataset size for each model:

Fine-tuning involves costs related to processing the specific datasets and the computational resources for retraining the model.

Fine-tuning a model like Snowflake Cortex can incur compute costs based on the number of tokens used in training. The cost is calculated per million tokens, and you can refer to the Snowflake Service Consumption Table for the exact cost in credits.

A token is the smallest unit of text processed by the Snowflake Cortex Fine-tuning function, equivalent to approximately four characters of text. The number of tokens can vary depending on the model.

For the COMPLETE function, which generates new text in the response, both input and output tokens are counted. This means you'll need to consider the cost of both the input and output text.

The cost of fine-tuning trained tokens is calculated as the number of input tokens multiplied by the number of epochs trained. You can use the FINETUNE ('DESCRIBE') (SNOWFLAKE.CORTEX) command to see the number of trained tokens for your fine-tuning job.

Here are the limits for training/validation dataset size for each model:

In addition to compute costs, you'll also need to consider normal storage and warehouse costs for storing the output customized adaptors, as well as any SQL commands you run.

Introducing biases is a real concern when fine-tuning models. Yes, it can happen if the fine-tuning dataset is not diverse or balanced.

A poorly curated dataset can perpetuate biases, making them even more entrenched. Careful dataset curation is crucial to minimize this risk.

Fine-tuning can also inherit biases from the pre-trained model, which is often trained on a large but potentially biased dataset.

Fine Tuning Models is an essential step in the machine learning process. Fine-tuning improves the efficiency, accuracy, and speed of AI models, making them more cost-effective and better suited to specific applications.

You have several base models available for fine-tuning, including llama3-8b, llama3-70b, and mistral-7b. Each model has its own strengths and weaknesses, and you should choose the one that best fits your task. For example, llama3-8b is ideal for tasks that require low to moderate reasoning with better accuracy.

To fine-tune a model, you need to prepare the training data, start the fine-tuning job with the required parameters, and monitor the training job. You can use the SNOWFLAKE.CORTEX.FINETUNE function to check the status of your tuning job. If you no longer need a fine-tuning job, you can cancel it using the SNOWFLAKE.CORTEX.FINETUNE function with 'CANCEL' as the first argument.

Here are some key benefits of fine-tuning:

Fine-tuning is essential in AI for several strategic reasons, primarily focusing on improving efficiency, effectiveness, and specific task performance. Fine-tuning smaller models for specific tasks can be highly cost-effective compared to operating larger, more generalized models.

Fine-tuning allows us to build upon a pre-trained model, significantly reducing the time and resources required to achieve good results. This is because pre-trained models have already learned many relevant features and patterns that can be beneficial for related tasks.

Fine-tuning models can be faster at performing specific tasks, with models that are fine-tuned on specific datasets typically much faster than larger, more generalized models. This is a significant advantage in many real-world scenarios where speed and time savings are crucial.

Fine-tuning sharpens a model's ability to handle particular domains or tasks with greater precision and relevance. This is because fine-tuning focuses on adapting the model to the specific task at hand, rather than trying to learn everything from scratch.

Here are some key benefits of fine-tuning:

Fine-tuning jobs are long running, which means they are not tied to a worksheet session. You can check the status of your tuning job using the SNOWFLAKE.CORTEX.FINETUNE function with 'SHOW' or 'DESCRIBE' as the first argument.

If you no longer need a fine-tuning job, you can use SNOWFLAKE.CORTEX.FINETUNE function with 'CANCEL' as the first argument and the job ID as the second argument to terminate it.

Fine-tuned models can be shared to other accounts with the USAGE privilege via Data Sharing.

Cross-region inference does not support fine-tuned models. Inference must take place in the same region where the model object is located. You can use database replication to replicate the fine-tuned model object to a region you want to make inference from if it's different than the region the model was trained in.

Here's a summary of how to manage fine-tuned models:

When you're ready to fine-tune a model, you've got several options to choose from. Each model is designed to excel in specific tasks, so let's take a look at what's available.

The llama3-8b model is ideal for tasks that require low to moderate reasoning with better accuracy than the llama2-70b-chat model, like text classification, summarization, and sentiment analysis.

You can also consider the llama3-70b model, which delivers state-of-the-art performance ideal for chat applications, content creation, and enterprise applications.

The llama3.1-8b model is a light-weight, ultra-fast model with a context window of 128K, making it perfect for tasks that require low to moderate reasoning.

The llama3.1-70b model is a highly performant, cost-effective model that enables diverse use cases with a context window of 128K.

Here's a quick rundown of the models available for fine-tuning:

The mistral-7b model is a good choice for tasks that need to be done quickly, such as summarization, structuration, and question answering, thanks to its 32K context window and low latency.

Fine-tuning a model on a specific task can be a powerful way to improve its performance. This is especially true when the task is as specific as hot dog recognition.

A ResNet model can be fine-tuned on a small dataset of images with and without hot dogs. This small dataset consists of thousands of images.

The model was pre-trained on the ImageNet dataset, which provides a solid foundation for fine-tuning. Fine-tuning a pre-trained model can save time and resources compared to training a model from scratch.

The goal of fine-tuning the model is to recognize hot dogs from images. This can be achieved by adjusting the model's weights to better fit the specific task at hand.