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.

pip install "soup-cli[train]"   # add [train] to fine-tune; bare `soup-cli` is the light CLI
soup init --template chat
soup train

Fine-tune an 8B model on a 4 GB laptop GPU. Layer streaming keeps the frozen base out of VRAM and feeds it to the GPU one decoder layer at a time. Measured on an RTX 3050 Laptop 4 GB: Llama-3.1-8B-Instruct + NF4 at 119.6 tok/s, 3.32 GB peak — bit-exact against a normal resident run. Opt-in (stream_layers: true) and still BETA — how it works · all measurements · paper

_{Llama-3.1-8B-Instruct + NF4, LoRA, batch 1, seq 512 on an RTX 3050 Laptop 4 GB — 3.32 GB peak, 119.6 tok/s. Full video (90s)}

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

v0.72.4 — align on a laptop: DPO, ORPO, SimPO and KTO over layer streaming. Layer streaming keeps the frozen base out of VRAM and feeds it to the GPU one decoder layer at a time. It used to support supervised fine-tuning only; now it runs the preference losses too.

• DPO's reference model is free. DPO needs a reference to compare against, and a second copy of the model would double memory and defeat the whole point. Soup uses the same streamed base with its adapters switched off — one set of weights, one stream. Measured on an RTX 3050 4 GB: streamed DPO peaked at 0.914× the supervised-fine-tuning peak. Forcing a real second model in the same test cost +730 MB — exactly one copy of the weights.
• KTO is not reference-free, however it is usually described: it picks its reference the same way DPO does, so it gets the same treatment. ORPO and SimPO genuinely are.
• Bit-exact against a normal, non-streamed run of the same loss — 0.0 difference, the bar every release in this series has to clear.
• The VRAM pre-flight knows a paired loss is twice the rows, because chosen and rejected go through the model as one tensor.
• Honest cost: the reference is free in memory, not in time — DPO reads the layer stack 1.52× as often per step as supervised fine-tuning does.
• grpo / ppo stay excluded on purpose: generation re-reads every layer per token, which is exactly what streaming cannot amortise.
• Still BETA.

# 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])"

soup reward synth references.jsonl -o reward.py --output-report calib.json

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

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 · GitHub Releases.

Quick Start

1. Install

# 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.

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

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:

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).

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.

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 helps most, and it costs nothing. If you would like to fund the work directly:

❤️ Donate — one-off, any amount (use Change amount on the checkout page). Payments are processed by Stripe under the maintainer's registered business, MePlay, Inc. — that name, not "Soup", is what appears on the checkout page and on your card statement.

Donations buy GPU time for the hardware-gated work — multi-GPU, 8B+ validation, Apple Silicon — that a single 4 GB laptop cannot reach.

The other way to move exactly those items is hardware itself. They ship behind honest "requires <hardware>" gates rather than unverified claims, so if you have access to a bigger box — or GPU credits going unused — running one of the help wanted issues and posting the numbers helps as much as funding the GPU time would. 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.

Contact

Bugs and feature requests belong in the issue tracker, questions in Discussions — both get answered faster and help the next person with the same problem.

For live chat, setup help, and everything that reads better as a conversation, join the Discord. Anything that should still be findable in six months belongs in Issues or Discussions — a Discord answer helps one person, an issue helps everyone who hits the same thing. The Code of Conduct applies there too.

For anything that does not fit in public — security reports (see SECURITY.md), Code of Conduct matters, or press — email team@trysoup.dev. That is the project address and the right one for anything Soup-related. makazanalpamys@gmail.com is the maintainer's personal address; it reaches the same person and is a fine fallback.

Citing Soup

Layer streaming — training an 8B model on a 4 GB laptop GPU by streaming the frozen base from host RAM one decoder layer at a time — is described in a preprint, together with the correctness protocol that verifies a streamed run is bit-exact against a resident one:

Makazhan, A. (2026). Exact Layer Streaming: LoRA Fine-Tuning of an 8B Model on a 4 GB Laptop GPU. Zenodo. https://doi.org/10.5281/zenodo.21771064

The measurement records behind every number in it are in benchmarks/, published as written — including the failures, the assumptions that turned out wrong, and the numbers that were measured and then discarded.

@misc{makazhan2026exact,
  title        = {Exact Layer Streaming: LoRA Fine-Tuning of an 8B Model on a 4 GB Laptop GPU},
  author       = {Makazhan, Alpamys},
  year         = {2026},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.21771064},
  url          = {https://doi.org/10.5281/zenodo.21771064}
}

License

Apache-2.0. Copyright © the Soup contributors.