Introduction to xTuring
xTuring is a powerful and versatile platform designed to simplify the process of fine-tuning large language models (LLMs) for personalized applications. Whether users are working with renowned models like Mistral, LLaMA, or GPT-J, xTuring provides a seamless interface for adapting these models to specific data and purposes. This ensures not only effective model customization but also data privacy and security, as all processes can occur on personal computers or secure private clouds.
Key Features
Data Ingestion and Preprocessing: xTuring can import data from various sources and convert it into a format that large language models can effectively utilize.
Scalable GPU Usage: The platform supports scaling from a single GPU to multiple GPUs, allowing for faster fine-tuning processes.
Cost-Efficient Methods: By employing memory-efficient strategies such as INT4 and LoRA fine-tuning, xTuring significantly reduces hardware expenses—potentially by up to 90%.
Method Exploration and Benchmarking: Users have the opportunity to experiment with various fine-tuning approaches to identify the most effective model for their needs.
Comprehensive Evaluation: xTuring offers tools to assess fine-tuned models against standard metrics, facilitating detailed performance analysis.
Installation and Usage
To install xTuring, users can simply execute the following command:
pip install xturing
With xTuring, integrating and fine-tuning a language model is straightforward:
from xturing.datasets import InstructionDataset
from xturing.models import BaseModel
# Load the dataset
instruction_dataset = InstructionDataset("./examples/models/llama/alpaca_data")
# Initialize the model
model = BaseModel.create("llama_lora")
# Finetune the model
model.finetune(dataset=instruction_dataset)
# Perform inference
output = model.generate(texts=["Why are LLM models becoming so important?"])
print("Generated output by the model: {}".format(output))
Recent Updates
- LLaMA 2 Integration: Newly supported configurations include off-the-shelf and precision-tuned modes.
- Evaluation Enhancements: Models can now be evaluated using the perplexity metric for any dataset.
- INT4 Precision: xTuring now supports fine-tuning with INT4 precision, ideal for reducing computational overhead.
- CPU Inference: Inference is now optimized for CPUs, including those on laptops, via integration with Intel technologies.
- Batch Processing: Efficiency is enhanced by processing data in batches, which optimizes the time taken for generating results.
Interactive Tools
xTuring provides both command-line and UI-based playgrounds for users to experiment interactively with models. These tools enhance learning and experimentation by offering user-friendly interfaces for model interaction.
Supported Models
xTuring supports a wide range of LLMs including, but not limited to:
- Bloom
- Cerebras
- DistilGPT-2
- Falcon-7B
- Galactica
- GPT-J
- GPT-2
- LLaMA
- LLaMA2
- OPT-1.3B
These models can be further adapted using specialized configurations such as LoRA, INT8, and INT4 for precision and efficiency improvements.
Contribution and Support
xTuring is an open-source initiative, inviting contributions and improvements from the community. Support is readily available through documentation, a dedicated Discord server, and guidance for contributions to the project.
Licensed under the Apache License 2.0, xTuring embraces collaboration to continuously evolve and enhance its offerings.