xTuring provides fast, efficient and simple fine-tuning of LLMs, such as LLaMA, GPT-J, GPT-2, and more. It supports both single GPU and multi-GPU training. Leverage memory-efficient fine-tuning techniques like LoRA to reduce your hardware costs by up to 90% and train your models in a fraction of the time.
Whether you are someone who develops AI pipelines for a living or someone who just wants to leverage the power of AI, this quickstart will help you get started with
xTuring and how to use
BaseModel for inference, fine-tuning and saving the obtained weights.
xTuring provides a solution to easily load and fine-tune some pre-trained models in less than 10 lines of code. Fine-tuning a pre-trained large language models (LLMs) comes with a lot of complexity and requires Machine Learning along with domain knowledge. Hence, it quite a non-intuitive task for beginners and business owners who lack practical knowledge in the field. This particular issue has been addressed by xTuring by providing a simple interface understandable with little to no knowledge of Python. It is as short and crisp as:
from xturing.datasets import InstructionDataset
from xturing.models import BaseModel
# Load a model of your choice
model = BaseModel.create('llama_lora')
# Prepare the training data
dataset = InstructionDataset('...')
# Fine-tune the model
# Test the fine-tuned model
output = model.generate(texts=["Why LLM models are becoming so important?"])
# Save the fine-tuned model
Please checkout the following steps to fully understand the above code: