🍜 Soup

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

Soup turns the pain of LLM fine-tuning into a simple workflow. One config, one command, done.

pip install soup-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.

Quick Start

1. Install

pip install soup-cli

2. Create config

# Interactive wizard
soup init

# Or use a template
soup init --template chat    # conversational fine-tune
soup init --template code    # code generation
soup init --template medical # domain expert

3. Train

soup train --config soup.yaml

That's it. Soup handles LoRA setup, quantization, batch size, monitoring, and checkpoints.

4. Test your model

soup chat --model ./output

5. Push to HuggingFace

soup push --model ./output --repo your-username/my-model

Config Example

base: meta-llama/Llama-3.1-8B-Instruct
task: sft

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

Data Formats

Soup supports these formats (auto-detected):

Alpaca:

{"instruction": "Explain gravity", "input": "", "output": "Gravity is..."}

ShareGPT:

{"conversations": [{"from": "human", "value": "Hi"}, {"from": "gpt", "value": "Hello!"}]}

ChatML:

{"messages": [{"role": "user", "content": "Hi"}, {"role": "assistant", "content": "Hello!"}]}

Data Tools

# Inspect a dataset
soup data inspect ./data/train.jsonl

# Validate format
soup data validate ./data/train.jsonl --format alpaca

Features

Feature	Status
LoRA / QLoRA fine-tuning	✅
SFT (Supervised Fine-Tune)	✅
DPO (Direct Preference Optimization)	🔜
Auto batch size	✅
Auto GPU detection (CUDA/MPS/CPU)	✅
Live terminal dashboard	✅
Alpaca / ShareGPT / ChatML formats	✅
HuggingFace datasets support	✅
Experiment tracking	🔜
Web dashboard	🔜
Cloud mode (BYOG)	🔜

Requirements

• Python 3.9+
• GPU with CUDA (recommended) or Apple Silicon (MPS) or CPU (slow)
• 8 GB+ VRAM for 7B models with QLoRA

Development

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

License

MIT