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Longlora

LongLoRA is a public Python research repository containing code and documents for the LongLoRA fine-tuning approach and LongAlpaca long-context language models. It also provides the LongAlpaca-12k instruction dataset and documentation for training, evaluation, local inference and user-deployed demos.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Longlora — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use LongLoRA when you need research code and model resources for fine-tuning or experimenting with long-context LLMs, or want to follow its documented local question-answering workflow over lengthy text.

Who it is for

  • Researchers exploring fine-tuning methods for long-context large language models.
  • Developers who want to run or adapt LongAlpaca models for questions about lengthy text.
  • Teams studying long-context instruction-following data and model resources.

Documented uses

  • Explore code and documentation for the LongLoRA fine-tuning method.
  • Fine-tune a language model for longer-context inputs.
  • Use LongAlpaca-12k and released LongAlpaca models in long-context research.
  • Ask questions about book or paper text with the documented local inference workflow.
  • Study the repository's training, evaluation and StreamingLLM inference materials.
Before you choose

Requirements & limits

The repository is public and identifies its code license as Apache-2.0. The README lists CC BY-NC 4.0 for LongAlpaca data and model weights; check each resource's terms separately.

  • The README documents Python dependency and FlashAttention installation before choosing a released or fine-tuned model.
  • LongAlpaca-12k comprises 9,000 long question-answer examples and 3,000 short examples sampled from Alpaca data.
  • The repository describes model resources ranging from 7B to 70B and context lengths from 8k to 100k; select resources according to the task and available environment.
  • The README reports that FlashAttention can reduce GPU memory use while making generation slower, and recommends reducing the streaming recent size if memory runs out.
  • Code, data and weights have different stated licenses.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the repository for local model use

Prepare the documented environment and select a released or fine-tuned model for the next stage.

Before you start

  • A GitHub fork and a local clone of the repository.
  • Python package installation using the repository's requirements file.
  • FlashAttention installed as described in the README.
  • A released model or a model fine-tuned with the project.

Steps

  1. Fork the repository and clone it locally.

  2. Install dependencies with the command below.

    Documented command
    pip install -r requirements.txt

  3. Install FlashAttention with the command below.

    Documented command
    pip install flash-attn --no-build-isolation

  4. Choose a released model or fine-tune a model.

Expected outcome

The repository dependencies are installed and a model is selected for the documented chat, demo or other usage stage.

Guide limitations

  • These setup steps do not themselves run inference or deploy a demo.
  • The README does not specify exact runtime or hardware requirements.
Documentation 1 ↗
Source-based practical guide

Ask a question about long text with local inference

Use the documented inference script to ask a question about supplied book or paper text.

Before you start

  • The repository setup and a local LongAlpaca model.
  • A text file containing the material to query.
  • Values for the question, context size and maximum generation length.

Steps

  1. Provide the model path with --base_model.

  2. Set the question, context size and maximum generation length.

  3. Pass the text file path with --material and run the documented inference.py workflow.

Expected outcome

The documented workflow submits a question alongside long text to a local LongAlpaca model.

Guide limitations

  • The README does not specify exact hardware requirements.
  • The example uses FlashAttention; the README notes its generation can be slower while saving GPU memory.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
LongloraExplore code and documentation for the LongLoRA fine-tuning method.LinuxThe repository is public and identifies its code license as Apache-2.0. The README lists CC BY-NC 4.0 for LongAlpaca data and model weights; check each resource's terms separately.
Pulse Of MotionEstimate a video's physical frame rate from its visual dynamics.LinuxThe repository is public. Its README describes a released checkpoint that downloads from Hugging Face on first inference, with a manual download option. The strongest internal model is not fully released; the README directs interested users to contact the project about access. No license information is provided in the repository evidence.
Neural Image AssessmentTrain the model using AVA images and rating annotations.Cloud, Linux, macOS, Windows
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
14 October 2026
Original website title
JIA-Lab-research/LongLoRA · Official homepage title · retained in its source language
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