Anyone interested in learning more about training Llama 2 might be interested in this quick guide and video tutorial on how you can use GPT-4 custom-made datasets to train Meta’s latest large language model. To help refine and make the process easier GPT-llm-trainer stands as a beacon of innovation, designed to simplify the complex process of creating data and training models.
This fantastic tool is specifically engineered to automate the intricate steps involved during the process of training large language models, which traditionally include collecting a dataset, cleaning it, formatting it correctly, selecting a model, writing the training code, and finally, training it. Watch the video below kindly created by Prompt Engineering to learn more about how you can automate the Llama 2 training process.
The GPT-llm-trainer is an experimental new pipeline that aims to train a high-performing task-specific model. The beauty of this system lies in its ability to abstract away all the complexity, making it as easy as possible to transition from an idea to a fully-trained, high-performing model.
Training Llama 2 using custom GPT-4 datasets
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The user simply inputs a description of the task at hand, and the system takes over. It generates a dataset from scratch, parses it into the correct format, and fine-tunes a LLaMA 2 model, all tailored to the user’s specific needs. The GPT-llm-trainer boasts a variety of features, including dataset generation using GPT-4. It creates a range of prompts and responses based on the provided use-case. The system also generates an effective system prompt for your model.
Once the dataset is generated, the system automatically splits it into training and validation sets. It then fine-tunes a model and prepares it for inference. The user defines the prompt, which is a description of what they want the trained AI to do. The more descriptive and clear the user can be, the better the results. The system requires access to the GPT-4 API to create the dataset.
The setup involves a Google Collab notebook, a GPU (preferably a paid account), and the open AI API key. Users need to provide a single prompt that describes what they want the AI to do, set the temperature (which controls the creativity of gpt4), and the number of examples.
The data creation process divides the dataset into a train and test set, with 90% of the data kept for training and 10% for validation or testing. The hyperparameters for training the model are defined, including the model name, dataset name, and new model name.
Prompt Engineering suggests using the Auto Train Advanced package from hugging face for fine-tuning, as it allows for training powerful models with a single line of code. A powerful GPU is needed for training, and the official llama 2 model can be used if the user has the access token.
In conclusion, the GPT-llm-trainer is a groundbreaking tool that simplifies the process of training models, making it accessible and efficient for users. It’s a testament to the power of innovation in the field of artificial intelligence.
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