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Simple LLM Finetuner

Simple LLM Finetuner is an open-source Gradio interface for preparing text datasets, configuring and running LoRA fine-tuning through Hugging Face PEFT, and evaluating inference with the resulting adapter. Its README documents a local Python workflow for Linux or WSL with CUDA and NVIDIA GPU support.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Simple LLM Finetuner — OpenViST project profile in AI Developer Tools & SDKs.
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Understand the project

Practical overview

When it fits

Use it when examining or following its documented local workflow for a small LoRA fine-tuning experiment and subsequent adapter inference, while accounting for the project’s stated maintenance status.

Who it is for

  • Developers and experimenters who want a UI for LoRA fine-tuning rather than configuring the documented workflow entirely through code.
  • Users who want to enter training samples in an interface and test a saved adapter’s inference there.

Documented uses

  • Prepare training data by pasting examples separated by two blank lines.
  • Adjust fine-tuning parameters and launch a LoRA run.
  • Select a saved adapter in the Inference tab and try model responses.
Before you choose

Requirements & limits

The repository identifies the project as MIT-licensed and links to Colab and Hugging Face Spaces, alongside instructions for running the interface locally.

  • The local setup calls for Linux or WSL, Python 3.10, CUDA and PyTorch; the README recommends an NVIDIA GPU with at least 16 GB VRAM.
  • The README says smaller sample lengths may work with less GPU memory and mentions a Colab Tesla T4 for small datasets and sample lengths of 256.
  • The README explicitly states that the project is effectively dead and recommends other tools.

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 local interface and train a LoRA adapter

Install the project on Linux or WSL, start its Gradio interface, and follow the README’s documented dataset-to-training steps.

Before you start

  • Linux or WSL
  • Python 3.10, CUDA 11.7 and PyTorch 2 with PyTorch CUDA 11.7, following the README’s Conda setup
  • A modern NVIDIA GPU with at least 16 GB VRAM is recommended; smaller sample lengths may work with less memory

Steps

  1. Create and activate a Conda environment, then install CUDA and PyTorch as shown in the README.

  2. Clone the repository, enter its directory, and install dependencies with pip install -r requirements.txt.

  3. Run python app.py and open http://127.0.0.1:7860/ in a browser.

  4. Paste training examples separated by two blank lines, enter a new PEFT adapter name, adjust sequence length or batch size for available GPU memory, and select train.

Expected outcome

The README says the adapter is saved in the local lora/ directory.

Guide limitations

  • The README says the project is effectively dead and recommends other tools.
  • GPU memory needs vary with sequence length and batch size; the README does not guarantee that a particular setup will complete training.
Documentation 1 ↗
Source-based practical guide

Try inference with a saved adapter

Use the interface’s documented inference stage to explore a LoRA adapter after training.

Before you start

  • A completed training run with an adapter saved in the lora/ directory
  • The app running locally in a browser

Steps

  1. Open the Inference tab after training.

  2. Select the LoRA adapter.

  3. Try prompts and review the model’s inference responses.

Expected outcome

The interface lets the user evaluate the selected adapter’s inference capabilities.

Guide limitations

  • This workflow depends on having completed training and saved an adapter.
  • The README describes the project as effectively dead.
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
Simple LLM FinetunerPrepare training data by pasting examples separated by two blank lines.Cloud, Linux, WebThe repository identifies the project as MIT-licensed and links to Colab and Hugging Face Spaces, alongside instructions for running the interface locally.
HyperposeCustomize pose-estimation architectures and datasets.Docker, Linux, macOS, Windows
Multi Task NlpDefine multiple tasks and their data and model settings in a YAML file.Linux, macOS, WindowsThe project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
lxe/simple-llm-finetuner · Official homepage title · retained in its source language
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