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Soup

Soup

Fine-tune and post-train LLMs in one command. No SSH, no config hell.

Website · Quick Start · Config · Docs · Commands · Models

PyPI Downloads Python 3.10+ Apache-2.0 License Tests CI Website

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 train

Why Soup?

Training LLMs is still painful. Even experienced teams spend 30-50% of their time fighting infrastructure instead of improving models. Soup fixes that.

  • Zero SSH. Never SSH into a broken GPU box again.
  • One config. A simple YAML file is all you need.
  • Auto everything. Batch size, GPU detection, quantization — handled.
  • Works locally. Train on your own GPU with QLoRA. No cloud required.

What's New

v0.71.22 — Perf & measure polish. Four live paths from the recent BETA lifts get tighter:

  • MiniLLM on-policy distillation is fast — the on-policy rollout (training.minillm_on_policy: true) now threads a KV cache so each step forwards only the new token instead of re-feeding the whole prefix, resolving the earlier O(L²) cost. A LoRA student activates the cache too (the PEFT wrapper is unwrapped before the cache check).
  • soup serve --mole uses a KV cache — each task adapter in a served MoLE keeps its own cache in lockstep, created fresh per request (no cross-request leak). Output is byte-identical to the previous no-cache path.
  • soup deploy autopilot --measure is real and frugal — a first-party transformers loader factory loads each candidate live (per-candidate quant config), scores the baseline once, and pre-validates the whole --measure-candidates list up front, so a typo fails before any model load instead of burning N loads or doubling peak VRAM.
  • Live-codec TTS via SNAC (Orpheus) — encoding raw audio at train time (data.format: audio) is validated for Orpheus: audio is duration- and byte-capped from soundfile.info before decoding into RAM, and read through a symlink-safe file descriptor.

Full history: CHANGELOG.md · GitHub Releases.

Quick Start

1. Install

pip install soup-cli            # light: CLI + config + data tools (no PyTorch)
pip install 'soup-cli[train]'   # add the training stack (torch, transformers, peft, trl, …)
pip install git+https://github.com/MakazhanAlpamys/Soup.git   # latest dev

soup init, soup data …, and the other data/inspection commands work on the light install. Fine-tuning (soup train) needs the [train] extra.

2. Create a config

soup init                       # interactive wizard
soup init --template chat       # or start from a template

Templates: chat, code, tool-calling, medical, reasoning, vision, kto, orpo, simpo, ipo, bco, rlhf, pretrain, moe, longcontext, embedding, audio.

3. Train, test, ship

soup train --config soup.yaml                 # LoRA, quantization, batching — all handled
soup chat  --model ./output                    # talk to your model
soup push  --model ./output --repo you/my-model

soup merge  --adapter ./output                              # merge LoRA into the base
soup export --model ./output --format gguf --quant q4_k_m   # GGUF for Ollama / llama.cpp

More export targets (ONNX, TensorRT, AWQ, GPTQ, BitNet) and deployment options live in docs/serving-and-export.md.

Configuration

A complete soup.yaml:

base: meta-llama/Llama-3.1-8B-Instruct
task: sft
# backend: unsloth  # 2-5x faster, pip install 'soup-cli[fast]'

data:
  train: ./data/train.jsonl
  format: alpaca
  val_split: 0.1

training:
  epochs: 3
  lr: 2e-5
  batch_size: auto
  lora:
    r: 64
    alpha: 16
  quantization: 4bit

output: ./output

config/schema.py is the single source of truth for every field. Advanced data, training, and PEFT options are documented under Documentation.

Documentation

The full feature reference lives in docs/. Start here:

Guide Covers
Training tasks & methods SFT, DPO/GRPO/PPO/KTO/ORPO/SimPO/IPO/BCO, tool-calling, PRM, pre-training, distillation, classification, vision/audio/TTS, unlearning, RAFT/RA-DIT, loop-hardening detectors
PEFT, long context & efficiency DoRA, LoRA+, rsLoRA, VeRA, OLoRA, NEFTune, PiSSA, ReLoRA, optimizer & PEFT zoo, LLaMA Pro, GaLore, YaRN/LongLoRA, packing, curriculum, auto-tuning
Performance & quantization QAT, FP8, Quant Menu (I + II), KV-cache, NVFP4, save formats, Cut Cross-Entropy, gradient checkpointing, kernels, activation offloading, multi-GPU / DeepSpeed / FSDP
Data engineering Formats, the Axolotl/LF-parity pipeline, data tools, synthetic generation & forge, quality scorecards, trace tooling, remote datasets, mixing, recipe DAGs
Evaluation & probes Eval design/gate, eval-gated training, benchmarks, NLG metrics, calibration, Elo arena, diagnose, post-train X-ray probes, A/B, drift, tunability, soup advise
Serving & export OpenAI-compatible server, batch inference, benchmarking, merge/export, Anthropic Messages endpoint, speculative decoding, deploy autopilot, Web UI, Agent Forge
Adapters, registry & governance Adapter lifecycle/management, model registry, Soup Cans, the data flywheel (soup loop), knowledge editing, steering, supply-chain controls (scan/sign/BOM/attest/audit/airgap)
Backends, platform & ops MLX/Unsloth backends, alternative hubs, HF Hub integration, autopilot, experiment tracking, plan/apply, env lockfiles, hardware-fit, completions, plugins, utility commands
Command reference The full soup command list
Supported models & extras Recommended model families, the VRAM size guide, the pip extras matrix

Data Formats

All formats are auto-detected from JSONL, JSON, CSV, Parquet, or TXT:

  • alpaca — {"instruction": ..., "input": ..., "output": ...}
  • sharegpt — {"conversations": [{"from": "human", "value": ...}, ...]}
  • chatml — {"messages": [{"role": "user", "content": ...}, ...]}
  • dpo / orpo / simpo / ipo — {"prompt": ..., "chosen": ..., "rejected": ...}
  • kto — {"prompt": ..., "completion": ..., "label": true}
  • llava / sharegpt4v (vision), audio, plaintext (pre-training), embedding, prm, pre_tokenized, video, multimodal

Full schemas and the Axolotl/LlamaFactory-parity data pipeline (remote URIs, streaming, sharding, interleaving, vocab expansion, document ingestion) are in docs/data.md.

Common Commands

soup train  --config soup.yaml        # train (SFT/DPO/GRPO/PPO/KTO/ORPO/SimPO/IPO/...)
soup infer  --model ./output --input prompts.jsonl   # batch inference
soup chat   --model ./output          # interactive chat
soup serve  --model ./output          # OpenAI-compatible API server
soup merge  --adapter ./output        # merge LoRA into the base model
soup export --model ./output --format gguf           # export for deployment
soup eval   benchmark --model ./output               # evaluate
soup data   inspect ./data/train.jsonl               # dataset stats
soup recipes list                     # 100+ ready-made model recipes
soup autopilot --model <id> --data d.jsonl --goal chat  # zero-config
soup doctor                           # check GPU / deps / environment

The complete command list is in docs/commands.md.

Supported Models

Soup works with any text-generation model on the HuggingFace Hub — if it loads with AutoModelForCausalLM, it works, zero config changes. Llama 3.x/4, Qwen 2.5/3, Gemma 3, Mistral, Mixtral, DeepSeek R1/V3, Phi-4, and 100+ others ship as ready-made recipes (soup recipes list).

VRAM Max model (QLoRA 4-bit) Example
8 GB ~7B Llama-3.1-8B, Mistral-7B
16 GB ~14B Phi-4-14B, Qwen2.5-14B
24 GB ~34B CodeLlama-34B, Yi-1.5-34B
48 GB ~70B Llama-3.3-70B
80 GB+ 70B+ (full) or MoE Mixtral-8x22B, DeepSeek-V3

Full model + vision tables and the optional-extras matrix are in docs/models.md.

Docker

Run Soup without installing CUDA or PyTorch locally (image published to GHCR on every release):

docker pull ghcr.io/makazhanalpamys/soup:latest
docker run --gpus all -v $(pwd):/workspace ghcr.io/makazhanalpamys/soup train --config soup.yaml
docker compose up   # or build locally

Requirements

  • Python 3.10+
  • GPU with CUDA (recommended), Apple Silicon (MPS), or CPU (experimental — very slow)
  • 8 GB+ VRAM for 7B models with QLoRA

All training tasks run on CPU for testing (quantization auto-disabled). Optional extras (train, all, fast, vision, qat, serve, serve-fast, ui, eval, deepspeed, liger, mlx, onnx, tensorrt, …) are listed in docs/models.md.

Troubleshooting

soup doctor    # GPU, system resources, dependencies, and version in one place
  • ImportError: DLL load failed while importing _C (Windows) — reinstall PyTorch for your CUDA version: pip install torch --index-url https://download.pytorch.org/whl/cu121.
  • soup version ≠ pip show soup-cli — multiple Python installs; use a virtualenv.

Development

git clone https://github.com/MakazhanAlpamys/Soup.git
cd Soup
pip install -e ".[dev]"

ruff check src/soup_cli/ tests/    # lint
pytest tests/ -v                   # unit tests (fast, no GPU)
pytest tests/ -m smoke -v          # smoke tests (downloads a tiny model, trains)

pre-commit install                 # optional: ruff lint+format on commit

See CONTRIBUTING.md for the full workflow and SECURITY.md to report a vulnerability.

License

Apache-2.0. Copyright © the Soup contributors.