How to Fine Tune LLMs Using DeepSpeed Without OOM Issues

DeepSpeed is a blazingly fast deep learning training platform that allows users to train models on multiple GPUs with ease. One of the things that sets DeepSpeed apart from other platforms is its ability to fine tune LLMs without OOM (out-of-memory) issues. In this post, we'll show you how to take advantage of DeepSpeed's LLM fine tuning capabilities without running into OOM problems.

The first thing you need to do is install DeepSpeed. You can find instructions for doing so here. Once DeepSpeed is installed, you need to configure it for your environment. The DeepSpeed documentation includes detailed instructions for doing so.

Once DeepSpeed is installed and configured, you can begin fine tuning your LLMs. The process is fairly straightforward:

1. Load the data you want to use for training into DeepSpeed's DataLoader.

2. Instantiate a DeepSpeedLLMFineTuner instance.

3. Call the DeepSpeedLLMFineTuner.tune() method, passing in the DataLoader and the desired parameters for tuning.

4. Wait for the tuning process to complete.

That's all there is to it! DeepSpeed makes it easy to fine tune your LLMs without having to worry about OOM issues.