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Class is in session
Fine-Tuning LLMs
5.0
(3 ratings)
5 Weeks
·Cohort-based Course
Achieve optimal performance from your fine-tuned LLM
Class is in session
Fine-Tuning LLMs
5.0
(3 ratings)
5 Weeks
·Cohort-based Course
Achieve optimal performance from your fine-tuned LLM
Previously at
Course overview
Fine-tuning that *actually* works
A properly fine-tuned LLM can achieve performance that outshines leading foundation models. However, achieving these results is not as simple as making an OpenAI API call. In this course, we'll demystify fine-tuning by systematically going step-by-step through the entire process.
Step 1: Curate Data
Step 2: Train Model
Step 3: Evaluate Results
Step 4: Apply Guardrails
Step 5: Deploy Model
Step 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.
Who is this course for
01
You want to evaluate whether fine-tuning would be beneficial for your company
02
You want to enhance your expertise as a software engineer or data scientist
03
You want to explore the world of open-source LLMs
What you’ll get out of this course
Explore Open-Source LLMs
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.
Curate High-Quality Data
Create a fine-tuning dataset that is ideal for your use case.
Adjust your dataset to handle nuances and edge cases.
Train a Model
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.
Evaluate Results
Design a test suite to systematically evaluate model results.
Identify weaknesses in your model and determine how to fix them.
Optimize Costs and Scale
Deploy your model using techniques such as quantization to reduce costs.
Optimize inference parameters for your use case.
Continuous Improvement
Analyze the production impact of your model.
Implement a feedback system to continuously improve results.
This course includes
9 interactive live sessions
Lifetime access to course materials
15 in-depth lessons
Direct access to instructor
5 projects to apply learnings
Guided feedback & reflection
Private community of peers
Course certificate upon completion
Maven Satisfaction Guarantee
This course is backed by Maven’s guarantee. You can receive a full refund within 14 days after the course ends, provided you meet the completion criteria in our refund policy.
Fine-Tuning LLMs
Course syllabus
Week 1
Sep 4—Sep 8
Events
- Sep
4
Fine-Tuning Basics
Fine-Tuning Basics
Wed, Sep 4, 4:00 PM - 5:30 PM UTC
- Sep
6
Week 1 Office Hours
Week 1 Office Hours
Fri, Sep 6, 4:00 PM - 5:00 PM UTC
Modules
- Fine-Tuning BasicsFine-Tuning Basics4 Lessons • 2 Projects
Week 2
Sep 9—Sep 15
Events
- Sep
11
Train Your Model (Option 1)
Train Your Model (Option 1)
Wed, Sep 11, 9:30 PM - 11:00 PM UTC
- Sep
12
Train Your Model (Option 2)
Train Your Model (Option 2)
Thu, Sep 12, 4:00 PM - 5:30 PM UTC
- Sep
13
Week 2 Office Hours
Week 2 Office Hours
Fri, Sep 13, 4:00 PM - 5:00 PM UTC
Modules
- Train Your ModelTrain Your Model3 Lessons • 1 Project
Week 3
Sep 16—Sep 22
Events
- Sep
18
Optimize Your Dataset
Optimize Your Dataset
Wed, Sep 18, 4:00 PM - 5:30 PM UTC
- Sep
20
Week 3 Office Hours
Week 3 Office Hours
Fri, Sep 20, 4:00 PM - 5:00 PM UTC
Modules
- Optimize Your DatasetOptimize Your Dataset3 Lessons • 1 Project
Week 4
Sep 23—Sep 29
Events
- Sep
25
Deploy Your Model
Deploy Your Model
Wed, Sep 25, 4:00 PM - 5:30 PM UTC
- Sep
27
Week 4 Office Hours
Week 4 Office Hours
Fri, Sep 27, 4:00 PM - 5:00 PM UTC
Modules
- Deploy Your ModelDeploy Your Model4 Lessons • 1 Project
Week 5
Sep 30—Oct 3
Modules
- Private Coaching SessionPrivate Coaching Session1 Lesson
5.0
(3 ratings)
What students are saying
What students are saying
Haard Shah
Scott Morris
7 Cups Increases Engagement 250% Through Fine-Tuning
Discover how 7 Cups fine-tuned an LLM to create an AI therapist that is 250% more engaging than leading proprietary LLMs.
Get the free case study
Meet your instructor
Scott Kramer
I've led AI at startups across every stage, from founding engineer through IPO (3 out of 3 successful exits).
After my last exit, I became a solopreneur and specialize in helping companies fine-tune LLMs.
This course is a culmination of my learnings from successfully fine-tuning LLMs for clients.
Course schedule
4-6 hours per week
Workshops
Four 90 minute sessions
Students will explore each module, investigate real-world case studies, and identify strategies to apply to their project.
Office Hours (Optional)
Four 60 minute sessions
Students will receive hands-on help with their fine-tuning projects.
Course Project
2 hours per week
Students will design and implement a fine-tuning use case based on their interests.
Each week, students will apply strategies learned during the workshop to improve their results.
This isn't just some toy project - students will be building a model with actual data.
Private Coaching Session
One 45 minute session
Upon completion of the course, each student will receive a coaching session to review their progress and discuss how to continue to advance their AI skills.
Learning is better with cohorts
Active hands-on learning
This course builds on live workshops and hands-on projects
Interactive and project-based
You’ll be interacting with other learners through breakout rooms and project teams
Learn with a cohort of peers
Join a community of like-minded people who want to learn and grow alongside you
Frequently Asked Questions
What happens if I can’t make a live session?
I work full-time, what is the expected time commitment?
What’s the refund policy?
Can I get a group discount?
What are the prerequisites?
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