**Soup** is a fine-tuning and post-training CLI for LLMs that reached **#7 on GitHub Trending today with 297 new stars**, standing at **1,700 stars and 263 forks**. It is **Apache-2.0** Python, installs as `pip install "soup-cli[train]"`, and has moved fast since the repository opened on **20 February 2026** — **v0.73.2 shipped on 15 August 2026**, six days after v0.73.0. The headline claim is the one worth checking: **fine-tune an 8B model on a 4 GB laptop GPU**. The mechanism is **layer streaming**, which keeps the frozen base out of VRAM and feeds it to the GPU one decoder layer at a time, so only the adapter occupies resident memory. The measurement published in the README is **Llama-3.1-8B-Instruct + NF4 on an RTX 3050 Laptop 4 GB at 119.6 tok/s and 3.32 GB peak**, described as bit-exact against a normal resident run and independently reproduced on an **H100 at 113.00 tok/s in the same 3.32 GB**. The project also flags its own caveat in the same paragraph: that throughput number was taken on **v0.72.2, before a v0.73.0 correctness repair that cost −4.8% at 32B**, and has not been re-run on a 4 GB card since. A Colab T4 notebook is provided that caps the process to 4 GB and asserts the streamed model is bit-identical to a normal one, and the work carries a **Zenodo DOI (10.5281/zenodo.21771064)**. The workflow is deliberately flat: `soup init --template chat`, then `soup train`. A single `soup.yaml` carries `stream_layers`, `quantization: 4bit` for NF4, `batch_size`, `stream_source: auto` (RAM when it fits, NVMe disk when it does not), and a `seed` added in v0.73.0. Batch size, GPU detection, and quantization are auto-selected. Preference training landed in **v0.72.4**: **DPO, ORPO, SimPO, and KTO** now run over layer streaming, and rather than loading a second reference model, Soup reuses **the same streamed base with its adapters switched off** — measured at **0.914x the SFT memory peak**, where forcing a real second instance cost **+730 MB, exactly one copy of the weights**. The honest cost is stated too: free in memory, not in time, since DPO reads the layer stack **1.52x** as often per step. `grpo` and `ppo` are excluded on purpose. The other half of the project is `soup ship`, a release gate that answers whether a tune actually got better. It scores against **seven bundled offline suites** covering MCQ, arithmetic, tool-calling, JSON validity, and safety/refusal, with exit codes that mean something: **0 = SHIP, 2 = DON'T SHIP, 3 = bad flags, 1 = runtime error**. `--emit-evidence` makes a run replay into an identical verdict, `--config` binds that evidence to the exact recipe (stale evidence exits 3), and `soup ship --push owner/repo#N` posts the verdict card on a pull request. The v0.73.2 notes are unusually candid about the gate's own bugs: `mini_tool_call` was effectively ranking brace hygiene and scored 0.225 for a model that got 40/40 right, while `mini_mmlu` scored Llama-3.1-8B at **0.423 — below a 0.5B model** — because the extractor did not understand `\boxed{C}`; that one went **0.423 to 0.731** after the fix. A new **`mini_over_refusal`** suite mirrors the safety axis so a model that refuses everything can no longer read as a safety improvement, and **`--noise-floor N`** re-runs the base N times and refuses to call any delta smaller than the measured spread significant. The caveats are real. Layer streaming is still **BETA and opt-in**, and the flagship throughput figure is acknowledged as stale. Python is pinned to **3.10-3.12** only — v0.73.0 added that upper bound because on 3.13+ pip resolved untested PyTorch wheels that crashed before Soup even ran. There is a documented regression worth knowing about: adapters trained with `stream_layers: true` on **v0.72.0 were inert**, saved under keys with an extra `.inner.` segment so every loader silently returned the untuned base, fixed in v0.72.1. With **90 open issues** and a release roughly every few days, this is a project whose speed is both its appeal and its risk — pin a version and verify the adapter loads.