By Yasmin El-Sayed Yasmin El-Sayed
on Open Source AI Models

LongLLaMA

Open-source long-context LLaMA for research

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What Is LongLLaMA?

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LongLLaMA is a research-preview large language model and PyTorch toolkit focused on scaling context length to 256k tokens and beyond. Built on top of OpenLLaMA and fine-tuned with the Focused Transformer (FoT) method, it shows how to adapt LLaMA-style models for efficient long-context understanding.

The repository includes pretrained research checkpoints, Colab notebooks, and example scripts for inference, evaluation, and fine-tuning. It also provides benchmarking results across multiple sequence lengths and tasks, illustrating both quality and scalability trade-offs.

LongLLaMA is intended for researchers and practitioners exploring long-context modeling rather than as a fully productized deployment stack.

Quick Snapshot

LongLLaMA lets researchers and advanced practitioners experiment with extremely long-context LLMs without building a custom training stack from scratch. By extending OpenLLaMA with the Focused Transformer method, it lowers the barrier to studying and prototyping long-context capabilities.

Works on
  • Linux
  • Mac
  • API
  • Other
Pricing Model
Free — LongLLaMA is an open-source research project on GitHub and can be used for free under its repository license. There is no advertised commercial pricing.
Fits on
Affiliate Program
We could not identify an affiliate program.
API Availability
LongLLaMA has an API available.
Key Features
  1. Experiment with 256k+ token context lengths
  2. Extend OpenLLaMA using Focused Transformer
  3. Leverage ready-made scripts and benchmarks
Audience
  • machine learning researchers
  • AI practitioners
  • LLM engineers
  • data scientists
  • academic labs
  • open-source contributors
URL
https://github.com/CStanKonrad/long_llama

Screenshot

LongLLaMA

Key Features of LongLLaMA

Long-context modeling

Supports sequence lengths of 256k tokens and beyond, enabling experimentation with extremely long-context language understanding.

Focused Transformer

Implements the Focused Transformer (FoT) method to adapt LLaMA-style architectures for efficient long-context training and inference.

OpenLLaMA integration

Builds on OpenLLaMA weights so users can extend a familiar LLaMA-style model instead of training from scratch.

Research checkpoints

Provides pretrained research checkpoints that allow immediate experimentation with long-context capabilities.

Example scripts

Includes PyTorch-based scripts and Colab notebooks for inference, evaluation, and fine-tuning workflows.

Benchmarking tools

Offers benchmarking results and utilities to compare performance across multiple sequence lengths and tasks.

Use Cases for LongLLaMA

Long-context research

Experiment with language models that handle 256k+ token sequences to study how context length impacts model behavior, quality, and scalability.

Benchmarking LLMs

Run provided evaluation scripts and benchmarks to compare LongLLaMA against baselines across different sequence lengths and tasks.

Prototype fine-tuning

Use the PyTorch training code and pretrained checkpoints to fine-tune long-context LLaMA-style models on domain-specific datasets.

Method development

Build on the Focused Transformer framework to explore new techniques for efficient long-context modeling in open-source environments.

Frequently Asked Questions

What is LongLLaMA and how is it different from LLaMA?

LongLLaMA is an open-source research-preview model built on OpenLLaMA and extended with the Focused Transformer method to handle very long contexts, up to 256k tokens and beyond. It focuses on long-context experimentation rather than being a general-purpose production model.

Is LongLLaMA free to use?

Yes. LongLLaMA is an open-source project hosted on GitHub and can be used for free under its repository license. There is no advertised commercial pricing.

Who should use LongLLaMA?

LongLLaMA is aimed at machine learning researchers, LLM engineers, data scientists, academic labs, and open-source contributors interested in studying and prototyping long-context language models.

Does LongLLaMA provide pretrained models?

Yes. The repository includes pretrained research checkpoints so you can immediately run inference, evaluation, and fine-tuning without training from scratch.

Can I fine-tune LongLLaMA on my own data?

Yes. LongLLaMA includes PyTorch training code, example scripts, and Colab notebooks that you can adapt to fine-tune the model on your datasets, especially for long-context tasks.

Is LongLLaMA suitable for production deployment?

The authors describe LongLLaMA as a research preview focused on exploring long-context modeling. It is not positioned as a fully productized deployment stack.

LongLLaMA · Our Verdict

LongLLaMA stands out as a focused research toolkit for pushing LLaMA-style models to very long contexts without reinventing the training pipeline. Its clear integration with OpenLLaMA and Focused Transformer (FoT), plus ready-made scripts and benchmarks, make it a strong option for labs and practitioners exploring long-context behavior rather than production deployment.

Reviews 3.8 (1)

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