This is the Linux app named Simple LLM Finetuner whose latest release can be downloaded as simple-llm-finetuner2.0sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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Simple LLM Finetuner
DESCRIPTION
Simple LLM Finetuner is a beginner-friendly interface designed to make the process of fine-tuning large language models more accessible by providing a simplified UI and workflow built around parameter-efficient techniques such as LoRA. It allows users to customize pre-trained models using relatively small datasets and modest hardware, making it feasible to experiment with LLM training even on consumer-grade GPUs or cloud environments like Google Colab. The tool includes a web-based interface where users can input datasets, configure training parameters, and run fine-tuning jobs without deep knowledge of machine learning pipelines. It leverages libraries such as Hugging Face PEFT to enable efficient adaptation of models by modifying only a subset of parameters, significantly reducing computational requirements. In addition to training, the platform provides inference capabilities so users can immediately test and evaluate their fine-tuned models within the same environment.
Features
- Beginner-friendly web interface for fine-tuning workflows
- Support for LoRA-based parameter-efficient training
- Dataset input and management directly within the UI
- Adjustable training and inference parameters
- Integrated inference environment for testing models
- Compatible with consumer GPUs and Colab environments
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/simple-llm-finetuner.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
