Lessons From Fine-Tuning Llama-2

In recent developments, large open language models have achieved remarkable advancements, unlocking new possibilities for commercially-scalable enterprise applications. Among these models, Meta's Llama-2 series has set a new benchmark for open-source capabilities. While comprehensive language models like GPT-4 and Claude-2 offer versatile utility, they often exceed the needs of specialized applications. This presentation will explore our insights gained from fine-tuning open-source models for task-specific applications, demonstrating how tailored solutions can outperform even GPT-4 in specialized scenarios. We'll also discuss how leveraging Anyscale + Ray's suite of libraries has enabled efficient fine-tuning processes, particularly in an era where GPU availability presents a critical bottleneck for many organizations.

Takeaways:

• Where to apply fine-tuning and when would it shine?

• How to set up an LLM fine-tuning problem?

• How does Ray and its libraries help with building a fine-tuning infrastructure?

• What does it take to do parameter efficient fine-tuning?

• How does Anyscale platform help with LLM-based fine tuning?

Find the slide deck here: https://drive.google.com/file/d/1UGlN...


About Anyscale
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Anyscale is the AI Application Platform for developing, running, and scaling AI.

https://www.anyscale.com/

If you're interested in a managed Ray service, check out:
https://www.anyscale.com/signup/

About Ray
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Ray is the most popular open source framework for scaling and productionizing AI workloads. From Generative AI and LLMs to computer vision, Ray powers the world’s most ambitious AI workloads.
https://docs.ray.io/en/latest/


#llm #machinelearning #ray #deeplearning #distributedsystems #python #genai