Master the Art of Fine-Tuning Your Dolly Model

Table of Contents

1. Introduction
2. Using Dolly GPT for Inference
3. Fine Tuning with LoRa
4. Dataset Used for Fine Tuning
5. Installation and Setup
6. Checking the Tokenizer
7. Loading the Clean Dataset
8. Setting up the Model
9. Hyperparameters and Training Configuration
10. Training the Model
11. Saving and Loading the Trained Model
12. Pushing the Model to the Cloud

Article

Introduction

In this article, we will explore the process of fine-tuning the Dolly GPT model using LoRa (Low Rank Adaption). We will walk through the steps of setting up the model, preparing the dataset, defining hyperparameters, training the model, and saving/loading the trained model. Additionally, we will discuss the possibility of pushing the model to the cloud for easy access. Let's dive in!

Using Dolly GPT for Inference

Before we Delve into the fine-tuning process, let's briefly touch upon using Dolly GPT for inference. In a previous video, we explored how to use Dolly GPT for inference and a notebook was provided for reference. If You haven't watched that video or reviewed the notebook, it's recommended to do so before proceeding with fine-tuning.

Fine Tuning with LoRa

Now, let's focus on the process of fine-tuning the Dolly GPT model using LoRa. LoRa, or Low Rank Adaption, involves adding adapters to the model to improve its performance. If you're interested in learning more about LoRa, you can refer to the paper Mentioned in this video, which explains its workings in Detail. The good news is that LoRa integration is built into the Hugging Face PFT library, which we will be using for our fine-tuning process.

Dataset Used for Fine Tuning

It's important to note that the dataset used for fine-tuning is the cleaned Alpaca dataset. The original Alpaca dataset had several issues, such as incorrect answers and formatting problems. Therefore, the dataset was cleaned by the community to ensure its accuracy. However, it's crucial to stress that this dataset is not intended for commercial applications. While we will be training a GPT-J model, using the dataset itself for commercial purposes is not permitted. It's recommended to use this dataset as a guide to Create your own similar dataset for non-commercial use.

Installation and Setup

Before we proceed with fine-tuning, let's ensure that we have all the necessary libraries and packages installed. In the code, we will first import the clean dataset repository and then proceed to install the latest versions of the Transformers library and the PFT library. Additionally, we will be using the Bits and Bytes library, which allows us to load models in eight-bit format, providing efficiency in memory usage. Excitingly, there are plans to support four-bit models in the near future as well.

Checking the Tokenizer

To start with the fine-tuning process, we need to check the tokenizer. The model and tokenizer we will be using are from the EleutherAI GPT-J-6B repository. The GPT-J model has been trained on an extensive amount of data, approximately 400 billion tokens. We can leverage the auto tokenizer provided by Hugging Face to easily integrate the tokenizer into our workflow, making it convenient for fine-tuning.

Loading the Clean Dataset

Next, we need to load the cleaned dataset using the Hugging Face dataset Package. We can load the dataset from a JSON file by utilizing the functionalities provided by the package. If you're interested in creating your own dataset, it's recommended to study the JSON format used for the Alpaca dataset and modify the code accordingly. We will also inject instructions and inputs into the Prompts for training the model.

Setting up the Model

In this section, we will set up the actual model for fine-tuning. This involves importing the necessary libraries such as PyTorch, Bits and Bytes, and the GPT-J for causal language modeling from the Transformers library. Additionally, we will prepare the model for eight-bit training using the PEFT library. We will also configure the LoRa adapters and specify the dropout rate for the adapters. It's worth mentioning that during training, we will freeze the rest of the model and only focus on training the adapters.

Hyperparameters and Training Configuration

Before commencing the training process, it's crucial to set up the hyperparameters and training configuration. We will determine the batch size, micro batch size, and gradient accumulation steps. The batch size defines the number of examples processed in one iteration, while the micro batch size specifies the number of forward passes during a single iteration. The gradient accumulation steps decide how many micro batches are processed before performing backpropagation and updating the weights. These parameters can be adjusted Based on the hardware and specific requirements.

Training the Model

Now, it's time to train the model. We will utilize the Transformers trainer, which handles the training process efficiently. We pass in the model, the training dataset, the micro batch size, the gradient accumulation steps, and other necessary configurations. It's common practice to start with a warm-up phase, gradually increasing the learning rate. In this case, we will warm up the learning rate for a hundred steps before training the model for two epochs. We can also set the number of checkpoints to save during training to track the model's progress.

Saving and Loading the Trained Model

Once the training is complete, we have the option to save the trained model for future use. If you want to save the model locally, you can use the provided code to save the model and the tokenizer. On the other HAND, if you prefer to push the model to the cloud, you will need to log into the Hugging Face hub and generate a Read-write token. This token allows you to save the model on the cloud for easy access. Keep in mind that when pushing the model to the cloud, only the LoRa adapters are uploaded, not the entire model. If you wish to use the model from the cloud, you will still need to download the base GPT-J model and combine it with the LoRa adapters.

Pushing the Model to the Cloud

Finally, we discuss the process of pushing the model to the cloud using the Hugging Face hub. By following the provided instructions and using your Hugging Face token, you can save the trained model to the hub. The AdVantage of this approach is that you can easily share the model with others or access it from different devices without the need to download and configure the entire model every time.

Overall, the process of fine-tuning the Dolly GPT model using LoRa provides an opportunity to customize and enhance the model's performance. By incorporating your own dataset and adjusting the hyperparameters, you can train a specialized model tailored to your desired task. Whether you choose to save the model locally or push it to the cloud, the flexibility and power provided by the Hugging Face ecosystem make it a convenient choice for natural language processing tasks.

Conclusion

In this article, we explored the process of fine-tuning the Dolly GPT model using LoRa. We discussed the necessary installations, dataset preparation, model setup, hyperparameters, training, and model saving/loading. Additionally, we provided insights into pushing the model to the cloud for easy access. Fine-tuning models like Dolly GPT with LoRa allows us to leverage the power of transfer learning and adapt the model to specific tasks. With the availability of libraries like Hugging Face and the ease of use they provide, fine-tuning models has become more accessible than ever before.

Highlights

• Fine-tuning the Dolly GPT model using LoRa
• Cleaned Alpaca dataset for training purposes
• Installation and setup of libraries and packages
• Checking the tokenizer and loading the dataset
• Setting up the model for training with LoRa adapters
• Defining hyperparameters and training configuration
• Training the model and saving it for future use
• Pushing the model to the cloud for easy access
• Leveraging Hugging Face ecosystem for better NLP tasks

FAQ:

Q: Can I use the Alpaca dataset for commercial applications? A: No, the Alpaca dataset used in this fine-tuning process is not intended for commercial use. It is recommended to create your own dataset similar to the Alpaca dataset for non-commercial purposes.

Q: Can I adjust the hyperparameters and training configuration? A: Yes, you can experiment with different hyperparameters and training configurations to optimize the performance of the fine-tuned model for your specific task.

Q: How can I save and load the trained model? A: You can save the trained model locally and load it using the Hugging Face model loading mechanisms. Alternatively, you can push the model to the Hugging Face hub and access it from there.

Q: Can I push the entire model to the cloud? A: No, when pushing the model to the cloud, only the LoRa adapters are uploaded. The base GPT-J model needs to be downloaded separately and combined with the LoRa adapters for inference.

Q: What advantages does fine-tuning with LoRa offer? A: Fine-tuning with LoRa allows you to enhance the performance of the Dolly GPT model by incorporating specialized adapters. This provides more flexibility and control over the model's behavior for specific tasks.