Join our new webinar “Unlocking the Power of SLM Distillation for Higher Accuracy and Lower Cost” on March 5th at 9AM PT  ||  Register today ->

ubiai deep learning
Contact us
ubiai deep learning

Fine-Tuning NLP Models Using Ubiai: A No-Code Solution for LLMs

APRIL 10th, 2025

Fine-tuning large language models to meet specific requirements is crucial to optimizing their performance. As we saw throughout this guide, this process traditionally requires a deep understanding of machine learning and programming. However, with Ubiai, a powerful no-code fine-tuning platform, this task becomes accessible to a wider audience, including those without a technical background. This section will walk you through how to fine-tune models using Ubiai.
 

Ubiai as a No-Code Fine-Tuning Platform

 
Ubiai is a no-code platform that simplifies the model fine-tuning process. It allows users to fine-tune Large Language Models (LLMs) for specific tasks, without needing to write any code. Whether you’re working on a task like name entity recognition, text classification, or any other NLP problem, Ubiai provides an easy-to-use interface that streamlines the entire fine-tuning process.
 

Step-by-Step Guide to Fine-Tuning with Ubiai

 
 
Sign Up and Log In
 
Start by creating an account on Ubiai. Once you’ve signed up, log in to access your dashboard, where you can manage your fine-tuning projects and track progress.
Upload Your Dataset
 
With your account set up, the next step is to upload your data. Ubiai supports various formats, such as CSV. Simply add your dataset into the platform to get started. After uploading your data, you’ll need to choose the specific task that the data will be used for.
 
Create Your Model
 
Ubiai offers a variety of pre-trained models to choose from, each designed to work well for a range of natural language processing tasks. Select the one that best suits your specific needs.
 
Configure Your Training Parameters
 
Once you’ve selected your model, Ubiai lets you adjust key parameters. You can use default settings for quick fine-tuning, or customize these settings to optimize the model for your particular dataset.
Start the Fine-Tuning Process
 
After configuring your settings, simply click the Training button. Ubiai will handle the heavy lifting, adapting the model to your dataset and fine-tuning it for optimal performance.
 
 
Monitor and Evaluate Performance
 
During training, Ubiai provides real-time performance metrics so you can track progress. After training is complete, the platform gives detailed results on how the model performed, allowing you to evaluate whether it meets your expectations.
 
Deploy Your Fine-Tuned Model
 
Once you’re satisfied with the fine-tuned model, you can deploy it directly using Ubiai’s API. Ubiai provides an API endpoint that allows you to integrate the fine-tuned model into your application or project. Ubiai’s comprehensive documentation will guide you through the steps to connect the model and begin using it.

Advantages of No-Code Platforms for Fine-Tuning

 
Accessibility for Non Technical Users: One of the key benefits of Ubiai is its accessibility. No longer do you need to be an expert in machine learning to fine-tune a model. Ubiai’s no-code approach opens the door for professionals from diverse fields—such as business analysts, marketers, and product managers—to apply machine learning models to their projects without the need for coding skills.
 
Faster Time to Deployment: Fine-tuning models traditionally takes time, requiring extensive knowledge of frameworks, coding, and debugging. With Ubiai, the process is expedited, allowing you to fine-tune models in a fraction of the time and deploy them quickly into production. This means faster iterations and more agile workflows.
 
Support for Experimentation: With Ubiai, experimenting with different configurations, datasets, and models is easy. You can quickly try out different approaches and observe how small changes affect the model’s performance. This rapid experimentation promotes innovation and allows you to find the most effective solution with ease.

Fine-tuning Best Practices

 

The Importance of Best Practices

 
Fine-tuning can be a delicate process where small decisions significantly impact the model’s performance and generalization. Following best practices ensures that the fine-tuning process is not only effective but also efficient, avoiding common pitfalls and maximizing the utility of your model. Even when using UbiAI we need actionable strategies to help us fine-tune models while maintaining stability, optimizing for specific task, and balancing computational and data constraints.
 

Understand Your Dataset

 
Before beginning the fine-tuning process, a thorough understanding of your dataset is crucial. Ensure your dataset is representative of the task and balanced across different classes or categories. If your dataset is small, consider data augmentation techniques to artificially expand its size and diversity.
 

Data Quality

 
Your training data should be carefully curated and cleaned. Remove obvious errors, inconsistencies, or inappropriate content. Each example should represent the kind of output you want your model to produce. For instance, if you’re training a model to write technical documentation, ensure your training data consists of well-written, accurate technical documents that follow your desired style guide.
The quality bar for LLM training data is exceptionally high because these models can pick up on subtle patterns, both good and bad. If your examples are inconsistent in style or quality, the model will learn those inconsistencies.
 

Hyperparameter Selection

 
Choosing the right hyperparameters is crucial for successful fine-tuning. Hyperparameters control how the model updates its weights, balances training stability, and generalizes to unseen data. Poorly chosen hyperparameters can lead to issues like underfitting or overfitting, wasting computational resources and yielding suboptimal results.
The most critical ones include:
 
 
  • Learning Rate: The learning rate determines how much the model adjusts its weights during training. Fine-tuning typically requires a smaller learning rate than training from scratch to avoid disrupting the pre-trained weights.
  • Batch Size: This affects memory usage and the stability of the training process. Smaller batch sizes can lead to noisier updates, while larger batch sizes may require more computational resources.
 
  • Dropout Rate: Dropout prevents overfitting by randomly deactivating neurons during training. Adjust the dropout rate to balance between underfitting and overfitting.
 
  • Optimizer: The choice of optimizer (e.g., Adam, SGD, or RMSprop) impacts how effectively the model converges. Fine-tuning may require experimentation with different optimizers to find the best fit for your task.
 
  • Weight Decay: This regularization parameter penalizes large weights, helping to prevent overfitting.
 

Evaluate and Test Thoroughly

 
After fine-tuning, evaluate the model on both validation and test datasets to ensure robust performance. Consider testing on out-of-distribution samples to assess generalization. By following these best practices, you can maximize the efficiency and effectiveness of your fine-tuning efforts, ensuring that your model performs well on the target task while avoiding common pitfalls.
 
 
Note: Fine-tuning LLMs is as much about preservation as it is about adaptation. Success comes from understanding what makes these models unique and approaching the process with appropriate care. Start with small, high-quality datasets, pay careful attention to your strategy, and evaluate thoroughly across multiple dimensions.
What are you waiting for?

Fine-tune Your Model for Free

Get in touch
ubiai deep learning
The Services provided are really great, we received a genuine advice and at very reasonable cost. all the work went hassle-free and no complication.

Features

Case Studies

Company

Legal

© 2023 UBIAI Web Services — All rights reserved.
close menu icon

Unlocking the Power of SLM Distillation for Higher Accuracy and Lower Cost​

How to make smaller models as intelligent as larger ones

Recording Date : March 7th, 2025

Unlock the True Potential of LLMs !

close menu icon

Harnessing AI Agents for Advanced Fraud Detection

How AI Agents Are Revolutionizing Fraud Detection

Recording Date : February 13th, 2025

Unlock the True Potential of LLMs !

close menu icon

Thank you for registering!

Check your email for the live demo details

see you on February 19th

While you’re here, discover how you can use UbiAI to fine-tune highly accurate and reliable AI models!

Explore UbiAI
close menu icon

Thank you for registering!

Check your email for webinar details

see you on March 5th

While you’re here, discover how you can use UbiAI to fine-tune highly accurate and reliable AI models!

Explore UbiAI
close menu icon

Fine Tuning LLMs on Your Own Dataset ​

Fine-Tuning Strategies and Practical Applications

Recording Date : January 15th, 2025

Unlock the True Potential of LLMs !