Soup

Soup

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. ```bash 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 **v0.72.3 — layer streaming grows up: more models, bigger batches, resume, and a disk tier.** Layer streaming keeps the frozen base out of VRAM and feeds it to the GPU one decoder layer at a time. v0.72.0–.2 kept the scope deliberately tiny to prove it worked; this release removes the training wheels. - **Six more model families** — Mistral, Gemma / Gemma 2 / Gemma 3, and Phi / Phi-3 — each verified **bit-exact** against the same checkpoint loaded resident, in bf16 *and* NF4. - **`batch_size` above 1, gradient accumulation, and `--resume`** all work now. - **A pre-flight that predicts peak VRAM and refuses a run that will not fit.** Streaming bounds the *weights*; the logits tensor is not bounded by it and scales with `batch × seq`. On a 152k-vocab model at batch 8 that single tensor measured **8.71 GB — 146× the entire layer-buffer pool.** The prediction was fitted to ten real runs and never under-predicts any of them. - **A throughput forecast measured on your card, in your session**, quoted as a range next to the SM clock it was taken at — not a number compiled into the source. - **A disk overflow tier.** When the base will not fit in RAM, `stream_source: auto` streams it from NVMe instead of refusing. Honest caveat: its *correctness* is verified bit-exact against the RAM tier, but **how much slower it is has not been measured** on the development hardware, and no figure is claimed. - Still BETA. ```yaml # soup.yaml — then just `soup train --config soup.yaml` training: stream_layers: true # base streams out of VRAM; only the adapter trains quantization: 4bit # NF4 — ~4x smaller store, so 8B fits a 4 GB card batch_size: 4 # v0.72.3: bigger batches amortise the weight read stream_source: auto # RAM when it fits, NVMe disk when it does not ``` > **Trained with `stream_layers: true` on v0.72.0?** That adapter is inert — its tensors were > saved under keys with an extra `.inner.` segment, so every loader returned the untuned base. > Fixed in v0.72.1; re-run or re-save. Check with: > `python -c "from safetensors.torch import load_file; print([k for k in load_file('adapter_model.safetensors') if '.inner.' in k][:3])"`
Previous release — v0.71.40, soup reward synth (generate a reward verifier from your data) Point `soup reward synth` at a JSONL of reference outputs and it infers a deterministic verifier, writes a readable / committable `.py` reward function, and — the part nobody else does — *refuses* to emit one that can't tell your references from bad answers (four families: `numeric` / `json_schema` / `regex` / `tool_call`; a mandatory calibration report is the moat). Reward ensembles (`reward_fn: "accuracy,format"`) also train now. (#311) ```bash soup reward synth references.jsonl -o reward.py --output-report calib.json ```
Previous release — v0.71.39, CI for weights not prompts (emit + provenance-bind the ship verdict) `soup ship`'s verdict became emittable, committable, and provenance-bound: `--emit-evidence` makes a run replay into an identical verdict, `eval.ship` in `soup.yaml` + `--config` makes the gate policy reviewable, and `--config` binds evidence to the exact recipe that produced it (stale evidence → exit 3). `soup ship --push owner/repo#N` posts the SHIP / DON'T-SHIP card on the PR.
Previous release — v0.71.38, The gate grows teeth (real leg-2 regression gate) `soup ship`'s regression leg became real: a fixed, extraction-based scorer over seven bundled, offline suites (MCQ · arithmetic · tool-calling · JSON validity · safety/refusal). A tune that wins your task but quietly breaks tool-calling now gets a **DON'T SHIP**. Zero new deps. ```bash soup ship --base ./base --adapter ./my-lora --task-eval my_task.jsonl # exit 0 = SHIP · 2 = DON'T SHIP · 3 = bad flags · 1 = runtime error ```
Previous release — v0.71.33, soup draft (measure speculative decoding) `soup draft measure` reports a draft model's **acceptance rate** + real plain-vs-assisted tok/s (exit 0/2/1 for CI); `soup draft distill` distils your target into a dense tiny draft, auto-wired into `soup serve --auto-spec`. The honest result on a small same-family pair: distillation didn't move acceptance (69.3% → 69.3%) and assisted decoding was a net slowdown — which is exactly the number you want *before* shipping speculative decoding. ```bash soup draft measure --target ./my-tuned-model --draft HuggingFaceTB/SmolLM2-135M-Instruct \ --prompts prod-prompts.jsonl # -> acceptance %, real tok/s, ship-or-not ```
Full history: [CHANGELOG.md](CHANGELOG.md) · [GitHub Releases](https://github.com/MakazhanAlpamys/Soup/releases). ## Quick Start ### 1. Install ```bash # Light core: CLI + config + data tools, no PyTorch pip install soup-cli # Add the training stack (torch, transformers, peft, trl, datasets, …) pip install "soup-cli[train]" # Everything (train + serve + ui + data) in one shot pip install "soup-cli[all]" # Or from GitHub (latest dev) pip install git+https://github.com/MakazhanAlpamys/Soup.git ``` The full extras table (`fast`, `mlx`, `serve`, `eval`, `ui`, `vision`, `audio`, …) lives in [`docs/models.md`](docs/models.md#optional-extras). > **Use double quotes around the extra.** They are the only spelling that works in > every shell — `cmd.exe`, PowerShell, bash, and zsh. > > Older tutorials and videos (including some of ours) show the single-quoted > `pip install 'soup-cli[train]'`. That is bash / zsh / PowerShell syntax, and it > fails on Windows `cmd.exe`, which has no single-quote quoting and hands the > quotes straight to pip: > > ``` > ERROR: Invalid requirement: "'soup-cli[train]'": Expected package name at the start of dependency specifier > ``` > > If you hit that, swap the `'` for `"` — pip is rejecting a literal quote > character, nothing is wrong with the package. (Dropping the quotes entirely > works on Windows too, but zsh then reads `[train]` as a glob and fails.) `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 ```bash 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 ```bash 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`](docs/serving-and-export.md). ## Configuration A complete `soup.yaml`: ```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). ## Documentation The full feature reference lives in [`docs/`](docs/). Start here: | Guide | Covers | |---|---| | [Training tasks & methods](docs/training.md) | 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](docs/peft-and-efficiency.md) | DoRA, LoRA+, rsLoRA, VeRA, OLoRA, NEFTune, PiSSA, ReLoRA, optimizer & PEFT zoo, LLaMA Pro, GaLore, YaRN/LongLoRA, packing, curriculum, auto-tuning | | [Performance & quantization](docs/performance-and-quantization.md) | QAT, FP8, Quant Menu (I + II), KV-cache, NVFP4, save formats, Cut Cross-Entropy, gradient checkpointing, kernels, activation offloading, layer streaming, multi-GPU / DeepSpeed / FSDP | | [Data engineering](docs/data.md) | Formats, the Axolotl/LF-parity pipeline, data tools, synthetic generation & forge, quality scorecards, trace tooling, remote datasets, mixing, recipe DAGs | | [Evaluation & probes](docs/evaluation.md) | 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](docs/serving-and-export.md) | OpenAI-compatible server, batch inference, benchmarking, merge/export, Anthropic Messages endpoint, speculative decoding (train + measure your own draft), deploy autopilot, Web UI, Agent Forge | | [Adapters, registry & governance](docs/adapters-and-governance.md) | Adapter lifecycle/management, model registry, Soup Cans, the data flywheel (`soup loop`), knowledge editing, steering, supply-chain controls (scan/sign/BOM/attest/audit/airgap) | | [Compliance & governance quickstart](docs/compliance.md) | HIPAA/SOC2/EU-AI-Act/SR-11-7 `init` templates, provenance (BOM/attest/repro-receipt), audit log, air-gap, model-card autogen (`soup card`), CI gate (`soup ci init`) | | [Backends, platform & ops](docs/backends-and-ops.md) | MLX/Unsloth backends, alternative hubs, HF Hub integration, autopilot, experiment tracking, plan/apply, env lockfiles, hardware-fit, completions, plugins, utility commands | | [Command reference](docs/commands.md) | The full `soup` command list | | [Supported models & extras](docs/models.md) | 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`](docs/data.md). ## Common Commands ```bash 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 --data d.jsonl --goal chat # zero-config soup doctor # check GPU / deps / environment ``` The complete command list is in [`docs/commands.md`](docs/commands.md). ## Supported Models Soup works with **any** text-generation model on the [HuggingFace Hub](https://huggingface.co/models?pipeline_tag=text-generation) — 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`](docs/models.md). ## Docker Run Soup without installing CUDA or PyTorch locally (image published to GHCR on every release): ```bash 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`](docs/models.md#optional-extras). ## Troubleshooting ```bash 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 ```bash 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](CONTRIBUTING.md) for the full workflow and [SECURITY.md](SECURITY.md) to report a vulnerability. ## Support Soup Soup is Apache-2.0 and free — and stays that way. It is built and maintained in the open on a single 4 GB laptop, which is why every performance number in these docs is measured rather than claimed. If Soup saved you a training run, [starring the repo](https://github.com/MakazhanAlpamys/Soup) helps most, and it costs nothing. The next most useful thing is **hardware**. Multi-GPU, 8B+ validation, and Apple Silicon are the parts a single 4 GB laptop cannot reach, so they ship behind honest "requires " gates instead of unverified claims. If you have access to a bigger box — or GPU credits going unused — running one of the [`help wanted`](https://github.com/MakazhanAlpamys/Soup/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22) issues and posting the numbers moves Soup further than anything else. Those issues say exactly what is blocked on hardware today. ## Contributors Built by the community ❤️ — thank you to everyone who has contributed. See [CONTRIBUTORS.md](CONTRIBUTORS.md). [![Contributors](https://contrib.rocks/image?repo=MakazhanAlpamys/Soup)](https://github.com/MakazhanAlpamys/Soup/graphs/contributors) ## License [Apache-2.0](LICENSE). Copyright © the Soup contributors.