Drive Soup (`soup-cli`), a CLI-first tool for fine-tuning and post-training LLMs with one YAML config and one command — SFT, DPO/GRPO/ORPO/SimPO/KTO, QLoRA/DoRA/LoRA+, layer streaming for 4-8 GB GPUs, eval-gated training, and serving. Use when the user wants to `soup init`/`soup train` a model, pick a training method or quantization scheme, estimate cost/memory before training, fine-tune on a small local GPU, migrate a config from Axolotl/LLaMA-Factory/Unsloth, or serve/merge/push a trained adapter. Triggers on: "soup-cli", "soup train", "soup init", "fine-tune an LLM locally", "QLoRA on a laptop GPU", "layer streaming", "soup advise", "soup autopilot", "DPO/GRPO/ORPO training", "merge LoRA adapter".
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name
soup
description
Drive Soup (`soup-cli`), a CLI-first tool for fine-tuning and post-training LLMs with one YAML config and one command — SFT, DPO/GRPO/ORPO/SimPO/KTO, QLoRA/DoRA/LoRA+, layer streaming for 4-8 GB GPUs, eval-gated training, and serving. Use when the user wants to `soup init`/`soup train` a model, pick a training method or quantization scheme, estimate cost/memory before training, fine-tune on a small local GPU, migrate a config from Axolotl/LLaMA-Factory/Unsloth, or serve/merge/push a trained adapter. Triggers on: "soup-cli", "soup train", "soup init", "fine-tune an LLM locally", "QLoRA on a laptop GPU", "layer streaming", "soup advise", "soup autopilot", "DPO/GRPO/ORPO training", "merge LoRA adapter".
allowed-tools
Bash Read Write Edit Glob Grep
compatibility
Python 3.10+. `pip install soup-cli` for the light CLI; `pip install "soup-cli[train]"` adds torch/transformers/peft/trl for actual training. GPU (CUDA or Apple MLX) recommended for training; CPU-only works for init/advise/profile/cost/data commands. Apache-2.0.
Soup turns fine-tuning into soup init → with a single YAML
config: task selection, quantization, batch size, and GPU/backend detection
are all handled for you. Its headline feature,
(), keeps the frozen base model out of VRAM and streams
it one decoder layer at a time, so an 8B model can fine-tune on a 4 GB laptop
GPU — measured bit-exact against a normal resident run.
soup train
layer streaming
stream_layers: true
When to use this skill
Standing up a new fine-tuning run (soup init, soup train) instead of
hand-rolling a Transformers/PEFT/TRL training script
Choosing a training method (SFT vs DPO/GRPO/ORPO/SimPO/KTO/IPO/BCO) or a
memory-saving scheme (QLoRA, DoRA, LoRA+, rsLoRA, layer streaming) for a
constrained GPU
Estimating training cost/memory (soup cost, soup profile) before
spending GPU hours, or getting a pre-flight method recommendation
(soup advise)
Migrating an existing Axolotl / LLaMA-Factory / Unsloth config into Soup
Serving, merging, or pushing a trained adapter (soup serve, soup merge,
soup push), or running the data-quality/eval tooling (soup data ...,
soup ship)
When not to use this skill
Training infrastructure at the Ray/DeepSpeed-cluster/multi-node scale as
the primary concern → use deepspeed or openrlhf-training directly;
Soup wraps DeepSpeed/FSDP as launch flags, not a replacement for them
Pure inference serving of an already-merged model with no training
involved → a plain inference-runtime skill is a better fit
The user is not touching Soup/PEFT/TRL at all (e.g. prompt engineering
only) → route to soup advise's own verdict (it may say PROMPT_ENG, not
training) rather than jumping straight into soup train
Instructions
Step 1: Install the right profile
bash
pip install soup-cli # light CLI only: init/advise/data/profile/cost
pip install "soup-cli[train]" # + torch/transformers/peft/trl for real training
Step 2: Decide the method before spending GPU hours
Do not default straight to soup train; advise/autopilot exist because
the wrong method (e.g. SFT when the data is a preference pair) wastes a full
run.
Step 3: Scaffold and edit the config
bash
soup init --template chat # or code/audio/... — see docs/models.md
soup fetch # pull a ready-made example config
For memory-constrained hardware, opt into layer streaming explicitly:
yaml
training:
stream_layers: true # base streams out of VRAM; only the adapter trains
quantization: 4bit # NF4
batch_size: 4
stream_source: auto # RAM when it fits, NVMe disk otherwise
Layer streaming is BETA and supports SFT plus DPO/ORPO/SimPO/KTO — not
GRPO/PPO (those re-read every layer per generated token, which defeats
streaming's amortisation).
doctor only inspects the environment (Python version, soup install,
[train] extras, CUDA/MPS availability) — it never installs packages or
starts a training run.
Best practices
Run soup advise/soup autopilot before soup train — picking the
wrong task family (SFT vs a preference loss) is discovered only after a
full training run otherwise.
soup cost/soup profile before renting a GPU — cheaper than
discovering an OOM or a $40 surprise after the fact.
Layer streaming is an opt-in trade, not a default — it trades memory
for extra layer-stack reads (DPO reads it ~1.52× as often as SFT); confirm
the method is on the supported list (SFT/DPO/ORPO/SimPO/KTO) before
enabling it.
Gate before you ship — prefer --gate evals/gate.yaml and soup ship
over eyeballing loss curves.
Heavy deps stay lazy — don't suggest importing torch/transformers/
peft/trl at module top in scripts driving Soup; the project itself
lazy-imports them so the light CLI stays fast.
Migrate configs, don't hand-port them — soup migrate --from axolotl|llamafactory|unsloth exists precisely to avoid manual config
translation errors.
