# Tag: tutorial

This tutorial is for anyone who wants to fine-tune powerful large language models such as Llama2, Mistral for their own projects.

This tutorial introduces an example of fine-tuning the open-source Llama3-8B model on the MoAI Platform.

Setting up the PyTorch execution environment on the MoAI Platform is similar to setting it up on a typical GPU server.

Once you have prepared all the training data, let's take a look at the contents of the train_llama3.py

Now, we will actually execute the fine-tuning process.

When you execute the train_llama3.py script as in the previous section, the resulting model will be saved in the

Let's rerun the fine-tuning task with a different number of GPUs.

From this tutorial, we have seen how to fine-tune Llama3 8B on the MoAI Platform.

This tutorial introduces how to fine-tune the open-source LLama3-70B model using the MoAI Platform.

To fine-tune the Llama3 70B model, you must use multiple GPUs and implement parallelization techniques such as Tensor Parallelism, Pipeline...

For a smooth tutorial experience, the following specifications are recommended:

Now, we will actually execute the fine-tuning process.

We have now explored the process of fine-tuning the Llama3-70b model on the MoAI Platform.

This tutorial guides you on fine-tuning the open-source Mistral 7B model on the MoAI Platform.

Preparing the PyTorch script execution environment on the MoAI Platform is similar to doing so on a typical GPU server.

Once you have prepared all the training data, let's delve into the contents of the train_mistral.py script to execute the actual fine-tuning process.

Now, we will train the model through the following process.

Running the train_mistral.py script, as in the previous section, will save the resulting model in the

Let's rerun the fine-tuning task with a different number of GPUs.

From this tutorial, we have seen how to fine-tune the Mistral 7B model on the MoAI Platform.

This tutorial guides you on how to fine-tune GPT-based models open-sourced by Hugging Face on the MoAI Platform.

Preparing the PyTorch script execution environment on the MoAI Platform is similar to doing so on a typical GPU server.

Once you have prepared all the training data, let's take a look at the contents of the train_gpt.py script to execute the actual fine-tuning process.

Now, we will train the model through the following process.

As in the previous chapter, when you run the train_gpt.py script, the resulting model will be saved in the

Let's rerun the fine-tuning task with a different number of GPUs.

So far, we have looked at the process of fine-tuning the GPT-based model from HuggingFace on the MoAI Platform.

This tutorial introduces an example of fine-tuning the open-source Qwen1.5 7B model on the MoAI Platform.

Preparing the PyTorch script execution environment on the MoAI Platform is similar to doing so on a typical GPU server.

If you've prepared all the training data, let's now take a look at the train_qwen.py script to actually run the fine-tuning process.

Now, we will train the model through the following process.

Running the train_qwen.py script, as in the previous chapter, will save the resulting model in the qwen_code_generation

Let's rerun the fine-tuning task with a different number of GPUs.

So far, we've explored the process of fine-tuning the Qwen1.5 7B model on the MoAI Platform.

The following tutorial will take you through the steps required to fine-tune Baichuan2 13B model with an example dataset, using the MoAI Platform.

Preparing the PyTorch script execution environment on the MoAI Platform is similar to doing so on a typical GPU server.

If you've prepared all the training data, let's now take a look at the contents of train_baichuan2_13b.py

Now, we will train the model through the following process.

Similar to the previous chapter, when you execute the train_baichuan2_13b.py script, the resulting model will be saved in the

Let's rerun the fine-tuning task with a different number of GPUs.

So far, we've explored the process of fine-tuning the Baichuan2 13B model, which is publicly available on Hugging Face, using the MoAI Platform.

This tutorial introduces an example of fine-tuning the open-source Llama2 13B model on the MoAI Platform.

Preparing the PyTorch script execution environment on the MoAI Platform is similar to doing so on a typical GPU server.

If you've got all your training data ready, let's dive into running the actual fine-tuning process using the

Now, we will train the model through the following process.

Upon running the train_llama2.py script as described earlier, the resulting model will be saved in the

Let's rerun the fine-tuning task with a different number of GPUs.

From this tutorial, we have seen how to fine-tune Llama2 13B for text summarization on the MoAI Platform.