Easiest Way to Fine-Tune LLMs with QLoRA, Flash Attention 2, and DeepSpeed

How do we determine whether a fine-tuned LLM will truly excel in your specific use case?

There is a sea of technical intricacies, and figuring that out is a lot of engineering hours.

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What we really want is all the latest techniques like QLoRA, FlashAttention 2, FSDP, GPTQ, and DeepSpeed, integrated into one seamless workflow, at the right level of abstraction.

That would be incredibly valuable for rapid prototyping and setting a solid foundation for production-level applications, right?

Luckily, you weren’t the only one thinking about this problem.

X-LLM, the brainchild of Boris Zubarev, is poised to redefine how we fine-tune LLMs. While being very flexible, it abstracts away the complexities of fine-tuning pipelines and empowers developers to concentrate on what truly matters: dataset quality, LLM evaluation and benchmarking.

In this article, I will walk you through:

• A brief explanation of the workflow
• Setting up X-LLM
• Fine-tuning Zephyr 7B Beta with QLoRA.

Zephyr 7B Beta is absolutely a killer. If you need a quick refresher, refer to my recent article: