Inside `MakazhanAlpamys/Soup`: One YAML File for Local LLM Fine-Tuning

Описание и применение

С помощью Soup разработчики могут тонко настроить 8‑битную модель на ноутбуке с 4 ГБ видеопамяти, используя единый YAML‑файл для конфигурации.

Задача

Fine‑tuning large language models locally is hampered by complex software stacks and high GPU memory requirements.

Результат

Using Soup’s YAML‑driven workflow, developers can fine‑tune an 8B model on a laptop GPU with ~4 GB VRAM, reducing setup complexity and improving reproducibility.

Рабочий процесс

• Clone the Soup repository
• Navigate to the repo and review README for installation
• Create a YAML file with fields: model, dataset, output_dir, training.batch_size, training.gradient_accumulation_steps, training.max_length
• Place the YAML file in version control
• Run the provided launcher using the YAML configuration
• Monitor training and evaluate results
• Iterate by editing the YAML and re‑running
• Verify checkpoint recovery and numerical stability on target GPU

Доказательства

Layer-streaming approach that can train an 8B model on a laptop GPU with approximately 4 GB of VRAM.

Источник

A single declarative YAML file defines model, dataset, output directory, and training hyper‑parameters.

Источник