Uploading Datasets to Hugging Face: A Comprehensive Guide
Part 1: Uploading a Dataset to Hugging Face Hub
Introduction
This guide provides a clear process for uploading a custom dataset to the Hugging Face Hub—a platform that facilitates developers in sharing and collaborating on datasets and models. We will transform a Python instruction-following dataset into a format compatible with training the latest Large Language Models (LLMs) and upload it for public access. Our focus will be on formatting the data to align with the Llama 3.2 chat template, preparing it for fine-tuning Llama 3.2 models.
Step 1: Installation and Authentication
To begin, you must install the necessary libraries and authenticate with the Hugging Face Hub:
- Use the command
pip install -q datasetsto install the datasets library. - Authenticate using
huggingface-cli login, where you will need your Hugging Face authentication token from your account settings.
This step ensures you can push content to the Hugging Face Hub securely.
Step 2: Load the Dataset and Define Transformation Function
Next, we will load an existing dataset and create a function to transform it into the Llama 3.2 chat format:
- Load your custom dataset (e.g.,
Vezora/Tested-143k-Python-Alpaca). - Define a transformation function that restructures each entry to fit the Llama 3.2 chat format, including a system prompt to guide the model’s behavior as a Python coding assistant.
This transformation is essential to ensure that the model understands the roles of system, user, and assistant in the conversation.
Step 3: Apply the Transformation to the Dataset
Apply the transformation function to the entire dataset:
- The
map()function processes each entry, resulting in a new dataset formatted for fine-tuning Llama 3.2. - This ensures the model can effectively interpret the conversation structure.
Step 4: Upload the Dataset to Hugging Face Hub
With the dataset prepared, you can now upload it:
- Use the
push_to_hub()method to upload your dataset, making it publicly available for others to use. - Once uploaded, you can view and manage your dataset on the Hugging Face Hub.
Part 2: Fine-tuning and Uploading a Model to Hugging Face Hub
Step 1: Install Required Libraries
To fine-tune large language models efficiently, install the necessary libraries:
- Use commands to install libraries such as
Unslothfor faster fine-tuning andTransformersfor model handling. - These tools enhance memory efficiency and performance during training.
Step 2: Load the Dataset
Load the dataset prepared earlier:
- Set a maximum sequence length for the model and load your dataset from Hugging Face.
Step 3: Load the Pre-trained Model
Now, load a quantized version of Llama 3.2:
- This process involves loading a 4-bit quantized version of the model, which reduces memory usage while maintaining performance.
Step 4: Configure Parameter-Efficient Fine-Tuning
Set up the model for fine-tuning using LoRA (Low-Rank Adaptation):
- This technique allows for efficient training with limited resources by adjusting a small number of parameters.
Step 5: Mount Google Drive for Saving
To save your trained model, mount your Google Drive:
- This ensures that your work is saved even if the session disconnects.
Step 6: Set Up Training and Start Training
Configure and initiate the training process:
- Create a Supervised Fine-Tuning Trainer with the defined model and dataset. Set parameters like batch size and learning rate to optimize training.
Step 7: Save the Fine-tuned Model Locally
After training, save your fine-tuned model:
- This allows for local storage and future access.
Step 8: Upload the Model to Hugging Face Hub
Finally, upload your fine-tuned model to the Hugging Face Hub:
- Utilize the appropriate method to merge and save your model, making it available for public use.
Conclusion
This guide demonstrates a full workflow for customizing AI models using Hugging Face. We transformed a Python instruction dataset into a format suitable for Llama 3.2 and fine-tuned the model efficiently. By sharing these resources on Hugging Face Hub, we contribute to the community and showcase the accessibility of AI development. This project illustrates how developers can create specialized models for specific tasks with relatively modest resources, highlighting the transformative potential of artificial intelligence in business.
