Introduction to X—LLM: Cutting Edge & Easy LLM Finetuning
X—LLM is a sophisticated yet user-friendly library designed to enhance the training and finetuning of Large Language Models (LLMs). It does this by using the most advanced methods available, including QLoRA, DeepSpeed, GPTQ, and Flash Attention 2, among others. The library is developed by Boris Zubarev and aims to streamline the training process, allowing users to focus on improving their models and data without the hassle of complex coding routines.
Why Choose X—LLM?
X—LLM is particularly valuable for those who need to train LLMs efficiently using cutting-edge techniques while reducing the time spent on coding. This library is particularly helpful for developing production-ready models and serving as a fast prototyping tool.
Key Features
- Efficient Training: Offers seamless and hassle-free training of LLM models.
- Data Integration: Easily integrates and processes new data.
- Effective Library Expansion: Simplifies the addition of features to the library.
- Size Reduction and Speed: Enhances training speed while concurrently reducing model sizes.
- Checkpoint Management: Automatically saves each checkpoint to the HuggingFace Hub.
- Easy Customization: Allows comprehensive customization of various training aspects.
- Progress Tracking: Integrates with Weights & Biases (W&B) to monitor training progress.
- Transformer Model Support: Compatible with many HuggingFace Transformer models, such as Llama 2 and OpenChat.
- Advanced Training Optimizations: Implements state-of-the-art techniques, including Flash Attention 2 and Gradient Checkpointing, to maximize training optimization.
Quickstart
To get started with X—LLM, ensure you have Python 3.8+, PyTorch 2.0.1+ and CUDA 11.8. Install the library using the following command:
pip install xllm
For a version more suited for training, use:
pip install "xllm[train]"
A recommended environment for training includes CUDA version 11.8 and the latest Docker image for transformers and PyTorch GPUs.
Fast Prototyping
X—LLM facilitates fast prototyping by allowing users to quickly initialize configurations, prepare datasets, and run experiments. Here's a brief example:
from xllm import Config
from xllm.datasets import GeneralDataset
from xllm.experiments import Experiment
# Configuration example with QLoRA
config = Config(
model_name_or_path="HuggingFaceH4/zephyr-7b-beta",
apply_lora=True,
load_in_4bit=True,
)
# Preparing dataset
train_data = ["Hello!"] * 100
train_dataset = GeneralDataset.from_list(data=train_data)
# Build and run experiment
experiment = Experiment(config=config, train_dataset=train_dataset)
experiment.build()
experiment.run()
Customization and Advanced Options
X—LLM provides various advanced options and configurations, allowing for deep customization. These include using LoRA, QLoRA, pushing checkpoints to HuggingFace Hub, reporting to W&B, employing Flash Attention 2, and leveraging DeepSpeed for multi-GPU setups.
Notebooks and Production Solutions
X—LLM is not just for prototyping; it’s also designed for building robust production solutions. The library offers comprehensive documentation, demo projects, and examples to guide you in integrating X—LLM into your projects.
Building Your Own Project
Starting with X—LLM involves setting up a dataset and integrating command-line tools. With rich resources, from guides to demo projects, users can learn to tailor X—LLM to their specific project needs effectively.
Conclusion
X—LLM is a key asset for developers who use LLMs, offering a perfect blend of cutting-edge techniques and user-friendly features to ensure efficient model finetuning, rapid prototyping, and production-level deployments. It's a powerful tool for anyone looking to harness the full potential of LLMs with minimal complexity.