Soup: fine-tune an 8B model on a 4 GB laptop GPU with one config and one command

Show HN: Fine-tune an 8B model on a 4 GB laptop GPU

Soup wraps LLM fine-tuning into a single-command workflow that runs on as little as 4 GB of VRAM. It uses QLoRA with Unsloth acceleration and supports mainstream 8B models like Llama, Mistral, and Qwen. You prepare a JSONL dataset, write a YAML config, and run `soup run`. A built-in Streamlit UI lets you test the model in the browser. The README doesn't disclose exact training speed or memory numbers, but it emphasizes that everything works on a regular laptop GPU.

Why it matters: A practical tool that lowers QLoRA fine-tuning to 4 GB VRAM and one command. Hits H and K, but it's a solo dev's fresh project with no community validation, so R is absent — lands right at the featured threshold of 72. If real-world benchmarks or community feedback appear late...