Sergio Perez & Harshita Seth

Adding knowledge to open-source LLMs

When is retrieval-augmented generation not enough? Learn the multi-stage process for deeply embedding new knowledge into an open-source LLM.

Adding knowledge to open-source LLMs
#1about 4 minutes

Understanding the LLM training pipeline and knowledge gaps

LLMs are trained through pre-training and alignment, but require new knowledge to stay current, adapt to specific domains, and acquire new skills.

#2about 5 minutes

Adding domain knowledge with continued pre-training

Continued pre-training adapts a foundation model to a specific domain by training it further on specialized, unlabeled data using self-supervised learning.

#3about 6 minutes

Developing skills and reasoning with supervised fine-tuning

Supervised fine-tuning uses instruction-based datasets to teach models specific tasks, chat capabilities, and complex reasoning through techniques like chain of thought.

#4about 8 minutes

Aligning models with human preferences using reinforcement learning

Preference alignment refines model behavior using reinforcement learning, evolving from complex RLHF with reward models to simpler methods like DPO.

#5about 2 minutes

Using frameworks like NeMo RL to simplify model alignment

Frameworks like the open-source NeMo RL abstract away the complexity of implementing advanced alignment algorithms like reinforcement learning.

Related jobs
Jobs that call for the skills explored in this talk.

Machine Learning Engineer

Picnic Technologies B.V.

Picnic Technologies B.V.
Amsterdam, Netherlands

Intermediate
Senior
Python
Structured Query Language (SQL)
+1

Matching moments

The hardware requirements for running LLMs locally
03:55 MIN

The hardware requirements for running LLMs locally

AI in the Open and in Browsers - Tarek Ziadé

The evolving role of the machine learning engineer
02:20 MIN

The evolving role of the machine learning engineer

AI in the Open and in Browsers - Tarek Ziadé

Why specialized models outperform generalist LLMs
05:09 MIN

Why specialized models outperform generalist LLMs

AI in the Open and in Browsers - Tarek Ziadé

Unlocking LLM potential with creative prompting techniques
04:59 MIN

Unlocking LLM potential with creative prompting techniques

WeAreDevelopers LIVE – Frontend Inspirations, Web Standards and more

Building an open source community around AI models
04:28 MIN

Building an open source community around AI models

AI in the Open and in Browsers - Tarek Ziadé

Building and iterating on an LLM-powered product
05:03 MIN

Building and iterating on an LLM-powered product

Slopquatting, API Keys, Fun with Fonts, Recruiters vs AI and more - The Best of LIVE 2025 - Part 2

Prompt injection as an unsolved AI security problem
07:39 MIN

Prompt injection as an unsolved AI security problem

AI in the Open and in Browsers - Tarek Ziadé

Exploring the role and ethics of AI in gaming
14:06 MIN

Exploring the role and ethics of AI in gaming

Devs vs. Marketers, COBOL and Copilot, Make Live Coding Easy and more - The Best of LIVE 2025 - Part 3

Featured Partners

Related Videos

See all videos
Inside the Mind of an LLM27:11

Inside the Mind of an LLM

Emanuele Fabbiani

Unlocking the Power of AI: Accessible Language Model Tuning for All31:50

Unlocking the Power of AI: Accessible Language Model Tuning for All

Cedric Clyburn & Legare Kerrison

LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices29:20

LLMOps-driven fine-tuning, evaluation, and inference with NVIDIA NIM & NeMo Microservices

Anshul Jindal

Self-Hosted LLMs: From Zero to Inference30:04

Self-Hosted LLMs: From Zero to Inference

Roberto Carratalá & Cedric Clyburn

Exploring LLMs across clouds28:38

Exploring LLMs across clouds

Tomislav Tipurić

Give Your LLMs a Left Brain27:35

Give Your LLMs a Left Brain

Stephen Chin

Large Language Models ❤️ Knowledge Graphs29:11

Large Language Models ❤️ Knowledge Graphs

Michael Hunger

Three years of putting LLMs into Software - Lessons learned26:30

Three years of putting LLMs into Software - Lessons learned

Simon A.T. Jiménez

Related Articles

View all articles
LM
Luis Minvielle
What Are Large Language Models?
Developers and writers can finally agree on one thing: Large Language Models, the subset of AIs that drive ChatGPT and its competitors, are stunning tech creations. Developers enjoying the likes of GitHub Copilot know the feeling: this new kind of te...
What Are Large Language Models?
KD
Krissy Davis
The Best Large Language Models on The Market
Large language models are sophisticated programs that enable machines to comprehend and generate human-like text. They have been the foundation of natural language processing for almost a decade. Although generative AI has only recently gained popula...
The Best Large Language Models on The Market

From learning to earning

Jobs that call for the skills explored in this talk.