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How to Fine-Tune Llama 2
How to Fine-Tune Llama 2? This guide will teach you how to train the Llama 2 model using fine-tuning.
Fine-tuning is like giving a computer brain a specialized course on a specific topic. Instead of starting from scratch, we build on what it already knows, making it even better for specific tasks.
This means businesses can have a model tailor-made for their unique needs, leading to more accurate and relevant results. It’s a smart way to get the best out of LLM’s like Llama 2.
Introduction
In artificial intelligence, fine-tuning models is crucial in achieving optimal performance. One such model that has recently gained significant attention is the Llama 2 model by Meta.
This article provides a detailed guide on fine-tuning the Llama-2 model, an LLM that has been making waves in the AI community.
The Importance of Fine-tuning
Before we dive into the specifics of fine-tuning a Llama 2 model, it’s important to understand what fine-tuning in machine learning (ML) entails.
Fine-tuning is a process where a pre-trained model, which has already been trained on a large-scale dataset, is further trained (or “fine-tuned”) on a smaller, specific dataset.
This process allows the model to adapt to specific tasks or domains without training the model from scratch.
The Llama 2 Model
The Llama 2 model is a large language model (LLM) developed by Meta. It’s a powerful open-source tool that can be used for various tasks, including text generation, translation, summarization, and more.
The model is pre-trained on a vast amount of text, making it a versatile tool for many natural language processing tasks.
We cover How to Install Llama 2 Locally in a previous guide, so if you haven’t already read that, be sure to check it out first.
Requirements
- Download the Auto Train Advanced package from HuggingFace’s GitHub repo. (Links provided)
- Ensure you have a Python version 3.8 or later.
- An Nvidia GPU is required for fine-tuning. If you don’t have a GPU, you can use Google Colab.
Getting Started with Fine-tuning
To begin fine-tuning the Llama-2 model, you must install the necessary packages and set up your environment.
This process can be done in a Google Colab notebook, a platform allowing easy access to powerful GPUs necessary for model training.
I personally only use Google Colab for small datasets otherwise, it will timeout and fail.
Or you can fine-tune the Llama 2 Model locally if you have a decent Nvidia GPU.
Step 1: Configure your Google Colab Notebook for LLama 2
This step is for fine-tuning Llama 2 on Google Colab Notebook if you want to set it up locally, skip to step 2.
Once you have created a new notebook, we must configure it to use a GPU. Go to Runtime and select ‘Change runtime type’.
Now choose the Python 3 Runtime type, select T4 GPU for the Hardware accelerator, and save.
Step 2: Install AutoTrain Advanced
One of the key tools in this process is the AutoTrain Advanced library from Hugging Face. This library is not limited to fine-tuning Llama 2.
Using tabular datasets, it can also fine-tune other models, including computer vision and neural network models.
Install the AutoTrain-Advanced Python package via PIP. Please note you will need Python >= 3.8 for AutoTrain Advanced to work properly. Use the following commands:
!pip install autotrain-advanced
!pip install huggingface_hub
Note: If installing AutoTrain locally, ‘!pip’ is not needed use pip instead.
Step 3: Install Git-LFS for Windows
Only for fine-tuning Llama 2 locally. Not needed for Google Coldab Envirornment. If you don’t already have git lfs installed, you must install it.
- Download the Windows installer from the git-lfs website.
- Run the Windows installer.
- Start a command prompt/or git for Windows prompt and run.
git lfs install
For more detailed instructions or other Operating Systems, check out the installation instructions for git lfs, on the Git-lfs site.
Step 4: Update Torch (Optional)
This step is for google collab if you are running this locally, it probably isn’t needed.
!autotrain setup --update-torch
Step 5: Apply HuggingFace Access Token
Now, we need to set up your HuggingFace access token.
- Go to your HuggingFace account and navigate to settings.
- Click on “Access Tokens” and create a new token or use an existing one.
- In your code, use the following to
from huggingface_hub import notebook_login notebook_login()
Then, add your token once successful, you should see ‘Token is valid’.
Step 6: Fine-Tuning Command For LLama 2
This is configured with my own project details. This is an example of how the single-line command to start the fine-tuning process should look:
Example Only
!autotrain llm --train --project_name 'llama2' --model TinyPixel/Llama-2-7B-bf16-sharded --data_path timdettmers/openassistant-guanaco --text_column text --use_peft --use_int4 --learning_rate 2e-4 --train_batch_size 2 --num_train_epochs 3 --trainer sft --model_max_length 2048 --push_to_hub --repo_id N0ker/llama2 --block_size 2048 > training.log
We use the timdettmers/openassistant-guanaco dataset to fine-tune Llama 2. You can change this to any other dataset you want to train your model on.
You will need to add your details to the single-line command we explain the details of the command in more detail later in the guide.
!autotrain llm --train --project_name your_project_name --model TinyPixel/Llama-2-7B-bf16-sharded --data_path your_data_set --use_peft --use_int4 --learning_rate 2e-4 --train_batch_size 2 --num_train_epochs 3 --trainer sft --model_max_length 2048 --push_to_hub --repo_id your_repo_id -
YouTube Fine-Tune Llama Guide
Here is a guide from Prompt Engineering that explains in a lot more detail how to train your own Llama 2 Model.
Llama 2 Fine-tuning Process Explained
The fine-tuning process involves several steps. First, you must specify that you want to fine-tune a large language model using the ‘llm‘ flag. Next, you must indicate that you want to train your model using the ‘train‘ flag.
You will then need to provide a project name and specify which model you want to fine-tune. In this case, you will use the ‘model’ flag and specify ‘Llama-2-7B-bf16-sharded‘ as the model to be fine-tuned.
It’s important to note that this method is not limited to fine-tuning Llama models. You can pick any Hugging Face model and use the same code to fine-tune it.
Next, you must specify the dataset you want to use to fine-tune the model. This can be done by providing the name of the dataset or the path to the dataset using the ‘data_path‘ flag.
The dataset should be in the form of a CSV file, and the path to the data should be the path of the folder where the dataset is located.
Formatting the Dataset
The format of the dataset is crucial for the fine-tuning process. The model expects the dataset to be in the Alpaca format, which involves having a single column that combines all the data.
This column, typically called the ‘text’ column, should include special tokens that indicate the input and output from the model.
Running the Training Process
You can run the training process once all the necessary elements are in place. This process involves defining the learning rate, which controls the speed of convergence during the training process, and setting the train batch size, which depends on the hardware you have available.
You will also need to define the number of training epochs and the trainer to be used. In this case, you will use ‘sft‘ for the trainer, which stands for supervised fine-tuning.
This means that you will be providing the dataset in an input-output format.
Conclusion
Fine-tuning the Llama-2 model is a comprehensive process that involves several steps and factors.
However, with the right tools and a clear understanding of the process, you can successfully fine-tune your own Llama-2 model and achieve optimal performance.
Remember, fine-tuning large language models like Llama-2 requires a powerful GPU. If you’re running this on a free Google Colab, it may time out unless you have a very small dataset.
Consider commenting on this post if you encounter any issues or want to discuss different fine-tuning methods. I will try and help troubleshoot your issues.