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

说明与用途

Soup 通过单一 YAML 配置文件，使开发者能够在约 4 GB 显存的笔记本 GPU 上微调 8B 模型，简化设置并提升可复现性。

问题

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

结果

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.

工作流

• 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

证据

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

来源

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

来源