This project demonstrates the process of fine-tuning the Falcon 7B model to generate descriptive prompts for MidJourney. We utilize advanced techniques like LoRA (Low-Rank Adaptation) and quantization to enhance model efficiency and scalability. The fine-tuning is performed in a Google Colab environment with T4 GPU support.
In this project, the Falcon 7B model is adapted to generate creative and contextually accurate MidJourney prompts. The key components of the project include:
- Fine-tuning a pre-trained model using advanced techniques
- Preparing and tokenizing a dataset of MidJourney prompts
- Training the model with customized parameters
- Evaluating and generating prompts with the fine-tuned model
This project was guided by AL Jason's tutorial on fine-tuning AI models, available on YouTube. His insights and instructions were invaluable for this implementation. You can view his guide here.
- Google Colab environment with GPU support (T4 GPU is recommended)
Install the required libraries by following the setup instructions in the Colab notebook.
Load and prepare your dataset as outlined in the notebook.
Configure and train the model using the provided instructions.
Save the trained model and load it for generating prompts as described in the notebook.