Fine-tune and post-train LLMs in one command. No SSH, no config hell.
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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 trainTraining 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.
v0.71.27 — Fine-tune Doctor: catch silent failures before they burn a training run. soup data doctor and soup data lint — no competitor (Unsloth/Axolotl/LlamaFactory) ships either.
- The #1 "model never stops generating" bug, caught pre-flight.
soup data doctorchecks whether every trained turn actually contains an EOS token — plus BOS duplication, no-system-role templates, unknown roles, and truncation risk — before you burn GPU hours on a broken chat template. - See exactly what's trained, token by token.
--show-mask Nrenders sample rows through the REAL collator path (answer-only / RAFT / packing-aware), so an assistant-mask bug is visible instantly instead of silently degrading the whole run. - Stop silently-worse DPO runs.
soup data lintflags length bias (the #1 silent preference-tuning degradation, reported as an effect size), near-duplicate pairs, chosen==rejected rows, and prompt leakage — across dpo/orpo/simpo/ipo/bco/kto data. - Found real bugs on a real tokenizer. Live-validated against
HuggingFaceTB/SmolLM2-135M-Instructon Windows + RTX 3050 — the smoke pass itself caught two genuine template-handling bugs before release.
soup data doctor train.jsonl --model meta-llama/Llama-3.1-8B-Instruct --show-mask 3
soup data lint prefs.jsonl --model meta-llama/Llama-3.1-8B-InstructFull history: CHANGELOG.md · GitHub Releases.
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 devsoup init, soup data …, and the other data/inspection commands work on the light install.
Fine-tuning (soup train) needs the [train] extra.
soup init # interactive wizard
soup init --template chat # or start from a templateTemplates: chat, code, tool-calling, medical, reasoning, vision, kto, orpo,
simpo, ipo, bco, rlhf, pretrain, moe, longcontext, embedding, audio.
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.cppMore export targets (ONNX, TensorRT, AWQ, GPTQ, BitNet) and deployment options live in
docs/serving-and-export.md.
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: ./outputconfig/schema.py is the single source of truth for every field. Advanced data, training,
and PEFT options are documented under 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 |
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.
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 / environmentThe complete command list is in docs/commands.md.
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.
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- 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.
soup doctor # GPU, system resources, dependencies, and version in one placeImportError: 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.
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 commitSee CONTRIBUTING.md for the full workflow and SECURITY.md to report a vulnerability.
Built by the community ❤️ — thank you to everyone who has contributed. See CONTRIBUTORS.md.
Apache-2.0. Copyright © the Soup contributors.