How to Fine-Tune Llama 3.1 70B with Ollama 0.6 and PyTorch 2.5

Large Language Models (LLMs) like Meta’s Llama 3.1 70B deliver state-of-the-art performance for general tasks, but fine-tuning is required to adapt them to domain-specific use cases. This guide walks through fine-tuning Llama 3.1 70B using Ollama 0.6 for model management and PyTorch 2.5 for training, with a focus on resource-efficient Parameter-Efficient Fine-Tuning (PEFT) via LoRA.

Prerequisites

Before starting, ensure you have:

Hardware: 4x NVIDIA A100 (80GB) GPUs or equivalent (70B models require ~140GB of VRAM for LoRA fine-tuning with 4-bit quantization)

Software: Ubuntu 22.04, Python 3.10+, CUDA 12.1+

Ollama 0.6 installed (follow official setup instructions )

Hugging Face account with access to Llama 3.1 70B (request access via Meta’s Hugging Face page )

Step 1: Set Up Ollama 0.6 and Pull Base Model

Ollama 0.6 simplifies local LLM management.…