Independent Developer Live-Streams At-Home Post-Training of 80B Parameter LLM
A developer on Reddit (u/jjusko20) is live-streaming an experiment to post-train the AliceAI-Foundation-80B base model into an instruction-tuned model from home using legacy Nvidia V100 GPUs. The training pipeline uses a custom synthetic dataset of 3,340 samples distilled from Qwen 3.8 27B over 96 hours of continuous generation. This project showcases how accessible large language model post-training and dataset distillation have become for individual enthusiasts using older enterprise hardware. It also highlights the growing trend of open, real-time public streaming of AI model training processes within the open-source community. The developer is fine-tuning a rank-16 QLoRA adapter targeting only the q/k/v/o projection matrices for 2 epochs, expecting the Supervised Fine-Tuning (SFT) phase to take 3 to 6 days before moving on to reinforcement learning. The dataset includes 1,760 general instruction samples and 1,580 agentic software engineering rows created via a Python sandbox tool-use environment.
## BACKGROUND
LLM post-training adapts a raw, completion-oriented base model into an instruction-following assistant capable of structured multi-turn conversations and function calling. Synthetic data distillation leverages larger or specialized AI models to generate high-quality training examples, replacing expensive manual human dataset annotation.