Fine-tune and post-train any open LLM in one command. 20 training methods (SFT, DPO, GRPO, PPO, KTO, ORPO, distillation, reward models), 142 model recipes, 9 quantization formats, multi-GPU via DeepSpeed ZeRO and FSDP2, and one-command export to GGUF, Ollama and HuggingFace. Two capabilities nothing else has: layer streaming, which streams the frozen base model from CPU RAM one decoder layer at a time so Llama-3.1-8B fine-tunes in 3.32 GB on a 4 GB laptop GPU — published as a preprint with a DOI (10.5281/zenodo.21771064); and a full training-provenance stack (CycloneDX ML-BOM, SLSA-3 attestation, EU AI Act Annex XI/XII docs, signed adapters, backdoor scanning). 153,000 lines of Python, 16,735 automated tests, 167 releases in five months, 78,000 downloads. Apache-2.0. trysoup.dev · github.com/MakazhanAlpamys/Soup