Discussing the article: "Integrate Your Own LLM into EA (Part 3): Training Your Own LLM with CPU"
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Check out the new article: Integrate Your Own LLM into EA (Part 3): Training Your Own LLM with CPU.
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
In the previous article of this series, we discussed the basic environment setup for running large language models and ran a simple LLM instance using llama.cpp in WSL. The most exciting part is that even without a powerful GPU, you can still run the example purely with a CPU! This series of tutorials will lower the hardware requirements as much as possible, striving to ensure that readers can try and verify the examples without being hindered by hardware issues. Of course, in our model training part, we will also introduce branches for different hardware platforms, including a pure CPU version and a version that supports AMD graphics card accelerated computing, believing that everyone will be able to try without hardware limitations.
Of course, you might wonder: Can models trained with a CPU be useful? What’s the significance of such models? Indeed, if you want to train a model with complex functions or to solve complex tasks using a CPU, it’s quite difficult, but it’s still possible to use it to implement some specific and relatively simple functions.
In this article, we will cover how to train a large language model with a CPU and create a financial dataset required for training large language models. This may involve knowledge mentioned in my other articles, which I will not repeat here. If readers wish to delve deeper, please read my related articles, where relevant links will be provided.
Author: Yuqiang Pan