facebookresearch/lingua

★ 4,760PythonAudience · researcherComplexity · 5/5Setup · hard

Mindmap

mindmap
  root((Meta Lingua))
    Purpose
      LLM training research
      Minimal codebase
    Architectures
      Llama standard
      Mamba alternative
      minGRU and minLSTM
    Infrastructure
      PyTorch
      Multi-GPU training
      SLURM cluster
    Features
      Checkpoint management
      Training speed metrics
      Data loading and shuffling

mindmap root((Meta Lingua)) Purpose LLM training research Minimal codebase Architectures Llama standard Mamba alternative minGRU and minLSTM Infrastructure PyTorch Multi-GPU training SLURM cluster Features Checkpoint management Training speed metrics Data loading and shuffling

Click or tap to explore — scroll the page freely

Things people build with this

USE CASE 1

Train a language model from scratch using the Llama architecture on a GPU cluster.

USE CASE 2

Swap in alternative sequence model designs like Mamba or minGRU to compare performance.

USE CASE 3

Use as a research baseline to benchmark new LLM training methods against standard architectures.

Tech stack

PythonPyTorchCUDASLURMHugging Face

Getting it running

Difficulty · hard Time to first run · 1day+

Requires NVIDIA GPUs and a SLURM-managed compute cluster, not suitable for consumer hardware or cloud notebooks.

In plain English

Meta Lingua is a research-focused codebase from Meta for training and running large language models, which are the AI systems behind tools like chatbots and text generators. The project is designed to be minimal and easy to modify, so that AI researchers can experiment with different model designs, training methods, and datasets without fighting through layers of complex infrastructure. The codebase is built on PyTorch, a widely used Python library for machine learning. It includes components for defining model architecture, loading and shuffling training data, distributing training across multiple graphics cards, managing checkpoints so training can be resumed after interruption, and measuring training speed. These components are kept separate and simple so that a researcher can swap one out or modify it without breaking the rest. The project includes several example applications that show how the components fit together. One trains a standard language model using the Llama architecture. Others demonstrate alternative model designs including Mamba, Hawk, minGRU, and minLSTM, which are different approaches to handling sequences of text that some researchers are exploring as alternatives to the standard transformer design. The README includes benchmark results showing how these different architectures compare on reasoning and knowledge tasks at the 1 billion and 7 billion parameter scales. Setting up Meta Lingua requires access to a machine with one or more NVIDIA GPUs and a compute cluster managed by SLURM, which is common in academic and industrial research settings. The setup scripts handle creating the Python environment and downloading training data from Hugging Face. This is a tool aimed squarely at machine learning researchers and engineers, not at end users or application developers.

Copy-paste prompts

Prompt 1

Using Meta Lingua, walk me through how to set up the environment, download training data from Hugging Face, and start a Llama model training job on a SLURM cluster.

Prompt 2

In the Meta Lingua codebase, how do I replace the default Llama architecture with the Mamba model design? Show me which files to modify.

Prompt 3

Using the Meta Lingua benchmark results, compare the 1B and 7B parameter versions of Llama vs Mamba on reasoning tasks, which architecture performs better?

Prompt 4

Show me how to resume training from a checkpoint in Meta Lingua after an interruption on a SLURM cluster.

Open on GitHub → Explain another repo

← facebookresearch on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.