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Meta Lingua
Overview :
Meta Lingua is a lightweight and efficient library for training and inference of large language models (LLMs) designed specifically for research purposes. It utilizes easy-to-modify PyTorch components, enabling researchers to experiment with new architectures, loss functions, and datasets. The library aims to facilitate end-to-end training, inference, and evaluation, providing tools for better understanding the speed and stability of the models. Although Meta Lingua is still under development, it already offers several sample applications demonstrating how to use this repository.
Target Users :
Target audience includes researchers, developers, and students in the fields of natural language processing and machine learning. The flexibility and ease of use of Meta Lingua make it an ideal tool for exploring novel LLM architectures and training strategies.
Use Cases
Researchers utilize Meta Lingua to train custom large language models for text generation tasks.
Developers leverage the library to optimize model performance and resource utilization in multi-GPU environments.
Students learn how to build and train large language models through Meta Lingua.
Features
Build models using PyTorch components that are easy to modify and experiment with new architectures.
Support for various parallel strategies, including data parallelism, model parallelism, and activation checkpointing.
Provides distributed training support for model training across multiple GPUs.
Includes a data loader for pre-training LLMs.
Integrates performance analysis tools to help calculate model memory and computation efficiency.
Supports model checkpoint management, allowing saving and loading models on different numbers of GPUs.
Offers configuration files and command line parameters for convenient experimental setup and iteration.
How to Use
1. Clone the Meta Lingua repository to your local machine.
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2. Navigate to the repository directory and run the setup script to create the environment.
3. Activate the created environment.
4. Use the provided configuration files or customize your own to launch the training script.
5. Monitor the training process and adjust configuration parameters as needed.
6. Use the evaluation script to assess the model at given checkpoints.
7. Use analysis tools to check the model's performance and resource utilization.
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