Steven Gonsalvez

Software Engineer

MakazhanAlpamys/Soup: Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU.

Why CEREBRO kept it

YAML-driven LLM fine-tuning, efficiency optimization

The text below is an automated extraction of the article at https://github.com/MakazhanAlpamys/Soup, stored verbatim in the public cerebro-vault repository. Copyright remains with the original publisher (github.com).

Fine-tune and post-train LLMs in one command. No SSH, no config hell. Website · Quick Start · Config · Docs · Commands · Models · Discord · Product Hunt Soup turns the pain of LLM fine-tuning into a simple workflow. One config, one command, done. pip install "soup-cli[train]" # add [train] to fine-tune; bare `soup-cli` is the light CLI soup init --template chat soup trainFine-tune an 8B model on a 4 GB laptop GPU. Layer streaming keeps the frozen base out of VRAM and feeds it to the GPU one decoder layer at a time. Measured on an RTX 3050 Laptop 4 GB: Llama-3.1-8B-Instruct + NF4 at 119.6 tok

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