WeAreDevelopers Logo WeAreDevelopers
Sign in
For You LIVE
World Congress 2024 • Aug 20, 2024 • Session details

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

Cedric Clyburn , @technicallylegare

Stop overpaying for massive, generic LLMs. Standard engineering teams can use InstructLab to locally fine-tune open-source models, drastically cutting inference costs without specialized data science expertise.

Pause
Mute Enter Fullscreen
#1 about 4 min

Moving beyond generalist models with local fine tuning

Adapting large language models for specific use cases addresses the limitations of generalist AI.

#2 about 3 min

Overcoming challenges and limitations of generative artificial intelligence

Knowledge cutoffs, lack of transparency, legal exposures, and deployment costs limit enterprise use of foundation models.

#3 about 5 min

Evaluating methods for improving large language model outputs

Prompt engineering, parameter efficient fine tuning, alignment tuning, and retrieval-augmented generation enhance model application performance.

#4 about 4 min

Selecting the appropriate foundation model for enterprise applications

Choosing appropriately sized and permissively licensed foundation models like Granite significantly reduces processing costs and deployment time.

#5 about 4 min

Simplifying open source model fine tuning with InstructLab

A community-driven project enables developers to contribute knowledge and skills to language models using simple YAML structures.

#6 about 9 min

Generating training data and fine tuning with InstructLab

Setting up a local taxonomy framework allows for synthetic data generation and parameter efficient model training.

#7 about 5 min

Integrating trained language models into enterprise Java applications

Serving a locally trained model within a Java web socket application provides tailored responses for specific business domains.

Matching moments

2:37 min

Understanding core parameters and mechanics of large language models

Julián Duque Julián Duque · WWC 2025

2:00 min

Navigating the layers of the language model inference stack

Christin Pohl Christin Pohl · WWC Europe 2026

5:01 min

Leveraging large language models for code optimization and development

Stephan Gillich Stephan Gillich +3 · WWC 2024

4:25 min

Overcoming AI hallucinations and restrictive content guardrails

Perf + AI

1:21 min

Cost considerations of fine-tuning large language models

Kevin Klues Kevin Klues

1:39 min

Balancing human-centric AI collaboration with environmental sustainability practices

Madalena Costa Madalena Costa · WWC 2024

More from World Congress 2024

Beyond the Hype: Real-World AI Strategies Panel thumbnail
24:07
10 months ago

Beyond the Hype: Real-World AI Strategies Panel

Mike Butcher, Jürgen Müller, Katrin Lehmann, Tobias Regenfuss

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation thumbnail
21:17
almost 2 years ago

Carl Lapierre - Exploring Advanced Patterns in Retrieval-Augmented Generation

Carl Lapierre

Coding an Immersive Copilot using Unity / .NET and Azure OpenAI! thumbnail
31:50
almost 2 years ago

Coding an Immersive Copilot using Unity / .NET and Azure OpenAI!

Zaid Zaim

Building a diverse tech company: The power of Diversity, Equity, Inclusion, and Belonging thumbnail
29:43
almost 2 years ago

Building a diverse tech company: The power of Diversity, Equity, Inclusion, and Belonging

Sue Quackenbush

Dapr in Practice thumbnail
24:08
almost 2 years ago

Dapr in Practice

marcklefter

Minimising the Carbon Footprint of Workloads thumbnail
27:46
almost 2 years ago

Minimising the Carbon Footprint of Workloads

Michael Mueller

Upcoming sessions on this topic

Open session

World Congress 2026 North America

Fast, Cheap, and Accurate: Optimizing LLM Inference with vLLM and Quantization

Legare Kerrison, Cedric Clyburn

Legare Kerrison
Cedric Clyburn
Open session

World Congress 2026 North America

No Single Model to Rule Them All: Building Resilient AI Agents Across Open & Closed LLMs

Emmanuel Acheampong

Senior Manager Developer Relations at Crusoe AI

Emmanuel Acheampong
Open session

World Congress 2026 North America

Understanding LLM Architectures: Inside the Design of Modern Models

Jofia Jose Prakash

Enterprise AI Architect at American Chemical Society

Jofia Jose Prakash
Open session

World Congress 2026 North America

You Can’t Re-Run Sunlight: Designing ML Data Architectures for Physical AI

An Phan

Senior Data Infrastructure Engineer @ Hippo Harvest

An Phan
Open session

World Congress 2026 North America

Building Stuff with GenAI - The Open Minded Workshop beyond OpenAI

Andreas Erben

CTO for Applied AI and Metaverse at daenet

Andreas Erben
Open session

World Congress 2026 North America

Making Science Larger, not just Faster

Yuval Dvir

Commercial Executive, SandboxAQ

Yuval Dvir

Related videos

Fine-Tuning Small Language Models for Agentic AI thumbnail
29:18
WWC Europe 2026 • about 1 month ago

Fine-Tuning Small Language Models for Agentic AI

Björn Buchhold

Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source thumbnail
29:50
WWC Europe 2026 • about 1 month ago

Cutting LLM Costs Without Cutting Quality: How to Beat Proprietary LLMs with Fine-Tuned Open Source

Viktoria Semaan

Self-Hosted LLMs: From Zero to Inference thumbnail
30:04
WWC 2025 • 12 months ago

Self-Hosted LLMs: From Zero to Inference

Cedric Clyburn, Roberto Carratalá

Inside the Mind of an LLM thumbnail
27:11
WWC 2025 • 12 months ago

Inside the Mind of an LLM

Emanuele Fabbiani

Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated thumbnail
30:40
WWC Europe 2026 • about 1 month ago

Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated

Christin Pohl

Adding knowledge to open-source LLMs thumbnail
25:35
WWC 2025 • 12 months ago

Adding knowledge to open-source LLMs

Harshita Seth, Sergio Perez

Related articles

From learning to earning

Jobs that call for the skills explored in this talk.