Finetune LLaMa 7b on RTX 3090 GPU - Tutorial

Here is a step-by-step tutorial on how to fine-tune a Llama 7B Large Language Model locally using an RTX 3090 GPU. This comprehensive guide is perfect for those who are interested in enhancing their machine learning projects with the power of Llama 7B. In this tutorial, I briefly walk through the entire process,setting up a Python virtual environment on your Ubuntu OS, launching a Jupyter Lab server, and connecting it to Google Colab. You have to install the necessary pip packages, ensuring that the NVIDIA utility CUDA is correctly installed, and that your CUDA-supporting PyTorch version can access CUDA. The model we're training is Llama2-7B, a model with 7 billion parameters using 13 gigabytes of space. Our dataset consists of 1000 samples of question-answer and instruct prompts in multiple languages. This was done on a Zotac Gaming Trinity OC RTX 3090 GPU which has 24GB of VRAM. You can upload the trained model to Hugging Face and serve your model on various hosts, including Amazon Titan, GCP with Vertex AI, and NVIDIA NeMo. For local inference, you can directly run the model using the transformers library in textgen webui. You can quantize a transformers model with jupyter notebook or quantize and convert it to one .gguf file with llama.cpp. I got 33 tokens/s, proving that local training and inference can be viable for prototyping on llms and AI models. Thanks for watching, remember to like and subscribe! Keywords: Llama 7B, Large Language Model, Fine-tuning, RTX 3090 GPU, Ubuntu, Pytorch