Brainiac Labs

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Explore an interactive notebook that demonstrates how to fine-tune GPT-4o mini, boosting customer support ticket classification accuracy from 69% to 94%!

GPT-4o mini Fine-Tuning Notebook

Discover how 7 Cups fine-tuned an LLM to create an AI therapist that is 250% more engaging than leading proprietary LLMs.

7 Cups Increases Engagement 250% Through Fine-Tuning

Engineers and engineering leaders who build, deploy, and scale software systems.

Data scientists who apply statistical and ML techniques to extract insights from data.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.

Achieve optimal performance from your fine-tuned LLM.

Scott Kramer

Fine-Tuning LLMs

developer

Senior Manager, Analytics

Data Scientist

Software Engineer

A properly fine-tuned LLM can achieve performance that outshines leading foundation models. However, achieving these results is not as simple as making an API call. In this course, we'll demystify fine-tuning by systematically going step-by-step through the entire process. Step 1: Curate DataStep 2: Train ModelStep 3: Evaluate ResultsStep 4: Apply GuardrailsStep 5: Deploy ModelStep 6: Improve Results For each step, I will provide proven strategies and real-world case studies from my experience as an AI consultant. When it comes to fine-tuning, the devil is in the details. Practitioners must be precise and exacting to reach peak performance. I will provide hands-on help so that students successfully build an end-to-end fine-tuning solution during the course project.

Fine-tuning that *actually* works

Course syllabus

1-2 modules per week with lessons walking you through the step-by-step process of fine-tuning an LLM. Each module includes recorded demo videos, coding examples, and additional resources to help you complete the next project.

Self-Paced Lessons

Students will design and implement a fine-tuned model of their choosing. Each week, students will apply strategies from the lessons to further improve their model.

Course Project

Live Q&amp;A calls with Scott and the rest of the cohort to answer questions and receive hands-on help with that week's projects.

Q&A with Scott

Each student will receive a live coaching session with Scott to review their project, receive personalized feedback, and discuss how to improve their AI skills further.

Private Coaching Session

Course schedule

Software engineers and data scientists who have dabbled with LLMs and are considering a career switch to AI.

AI and ML engineers who want to enhance their skillset.

Startups who are evaluating whether fine-tuning would be beneficial for their company.

Who is this course for

What you’ll get out of this course

Create a fine-tuning dataset that is ideal for your use case. Adjust your dataset to handle nuances and edge cases.

Curate High-Quality Data

Apply the QLoRA algorithm to fine-tune a model (and understand what's actually happening under the hood!). Conduct hyperparameter tuning to optimize training for your use case.

Train a Model

Design a test suite to systematically evaluate model results. Identify weaknesses in your model and determine how to fix them.

Evaluate Results

Deploy your model using techniques such as quantization to reduce costs. Optimize inference parameters for your use case.

Optimize Costs and Scale

Analyze the production impact of your model. Implement a process to continuously improve results.

Build a Data Flywheel

Identify the pros and cons of various open-source LLMs and evaluate how their performance compares to the leading proprietary models. Analyze the current state of LLMs and where they are likely to go in the future.

Learn directly from Brainiac Labs

Brainiac Labs

Courses

Fine-Tuning LLMs

Resources

7 Cups Increases Engagement 250% Through Fine-Tuning

GPT-4o mini Fine-Tuning Notebook