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World Congress 2025 • Aug 20, 2025 • Session details

Adding knowledge to open-source LLMs

Harshita Seth , Sergio Perez

How do you inject proprietary knowledge into open-source LLMs? Master the pipeline of continued pre-training, supervised fine-tuning, and modern preference alignment to build tailored, domain-specific AI.

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#1 about 2 min

The core stages of large language model training

Understanding the pre-training and alignment stages establishes a foundation for how language models process and refine information.

#2 about 2 min

Why open-source language models need continuous knowledge updates

Updating models with current affairs and domain-specific knowledge prevents information degradation and improves task relevance.

#3 about 5 min

Using continued pre-training for domain-specific model adaptation

Applying self-supervised learning during continued pre-training ensures foundational models adapt accurately to highly specialized industry domains.

#4 about 7 min

Adding reasoning capabilities through supervised fine-tuning tasks

Structuring instruction datasets with chain-of-thought traces teaches neural networks to formulate explainable reasoning steps before answering.

#5 about 9 min

Aligning behavior and human preferences with reinforcement learning

Implementing comparative reward models and direct preference optimization aligns generative textual outputs with human behavioral expectations.

#6 about 2 min

Orchestrating model alignment with the NeMo RL framework

Adopting specialized automation frameworks simplifies the integration of complex reinforcement learning loops without engineering custom training layouts.

Matching moments

5:01 min

Leveraging large language models for code optimization and development

Stephan Gillich Stephan Gillich +3 · WWC 2024

2:37 min

Understanding core parameters and mechanics of large language models

Julián Duque Julián Duque · WWC 2025

2:08 min

Applying large language models to infrastructure tasks

Alfonso Sandoval Rosas Alfonso Sandoval Rosas · Europe 2026 Virtual

2:05 min

Enhancing language models with retrieval-augmented generation

Mary Grygleski Mary Grygleski · LIVE

1:37 min

Extending large language models without expensive retraining

Damir Damir · WWC 2025

1:16 min

Maturing through the machine learning development lifecycle

Xavier Portilla Edo · LIVE

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