The Simple LLM Finetuner is your go-to solution for fine-tuning language models! Whether you’re a beginner in machine learning or just exploring the realm of AI, this user-friendly interface allows you to efficiently utilize LoRA with the PEFT library on NVIDIA GPUs. In this article, we’ll guide you step-by-step on how to get started with Simple LLM Finetuner.

Getting Familiar with the Project

This project is effectively no longer maintained, but there are several other alternatives you can explore:

• LLaMA-Factory
• unsloth
• text-generation-webui

Features of Simple LLM Finetuner

• Paste datasets easily in the UI, separated by double blank lines.
• Adjust parameters for fine-tuning and inference as per your requirements.
• The beginner-friendly UI provides ample explanations for each parameter.

Getting Started

Prerequisites

Before diving in, make sure your setup meets the following requirements:

• Operating System: Linux or WSL
• GPU: Modern NVIDIA GPU with at least 16 GB VRAM (but you might be able to run with less for smaller sample lengths)

Installing the Required Packages

It is recommended to create a virtual environment to manage dependencies efficiently. Here’s how:

• Create and activate a virtual environment:

conda create -n simple-llm-finetuner python=3.10
conda activate simple-llm-finetuner

• Install the necessary packages:

conda install -y cuda -c nvidia
conda install -y pytorch=2 pytorch-cuda=11.7 -c pytorch

• If on WSL, install CUDA manually using these steps and run:

export LD_LIBRARY_PATH=/usr/lib/wsl/lib

Setting Up and Training

Now that your environment is ready, you can clone the repository and install the final dependencies:

git clone https://github.com/lxe/simple-llm-finetuner.git
cd simple-llm-finetuner
pip install -r requirements.txt

Launch the app:

python app.py

Finally, open http://127.0.0.1:7860 in your browser. Prepare your training data by ensuring each sample is separated by two blank lines. Paste your entire training dataset into the textbox. Specify a name for your new LoRA adapter in the provided textbox and click “train.”

Be mindful to adjust the max sequence length and batch size according to your GPU memory limitations to ensure smooth operation. Once training is complete, navigate to the Inference tab, select your LoRA, and explore!

Understanding the Code: An Analogy

Imagine you’re a chef planning to create a unique dish. The base of your dish is like the pre-trained language model, while the ingredients you select represent your training data. Just as every ingredient impacts the flavor of your dish, each data sample influences how the language model learns. By adjusting the cooking temperature and time (like adjusting max sequence length and batch size in our model), you ensure that every element merges perfectly to create a masterpiece. The end result, your signature dish, is analogous to the fine-tuned model ready for use!

Troubleshooting

If you encounter issues during installation or while running the model, here are some troubleshooting tips:

• Ensure that your NVIDIA drivers are up to date.
• Check if the virtual environment is activated before running the app.
• If the application does not load, double-check that the ports used are not blocked or in use by another application.
• Explore available resources to address CUDA compatibility issues, especially on WSL.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Further Resources

If you’re looking for visual guidance, consider watching the YouTube walkthrough for a step-by-step guide on using the Simple LLM Finetuner.

Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.