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SahilKumar75/TuneOS

title TuneOS
colorFrom blue
colorTo indigo
sdk docker
pinned true
app_port 7860

TuneOS

CI Release License Python Code style: Ruff

TuneOS is a fine-tuning workstation for large language models. You bring a model and a dataset; TuneOS handles dataset prep, training, evaluation, and pushing the adapter to the Hub. It runs as a macOS desktop app or as two Docker containers on Hugging Face Spaces — compute stays on infrastructure you own.

Getting started

You need Python 3.10+, Poetry, and Docker Desktop.

git clone https://github.com/SahilKumar75/TuneOS
cd TuneOS
cp .env.example .env        # add HF_TOKEN for gated models
poetry install
docker compose up -d        # starts Redis + Celery worker
poetry run reflex run

Open http://localhost:3000. For your first run, try EleutherAI/pythia-410m — it trains on CPU and finishes in a few minutes.

No Docker? Start Redis and the worker manually:

redis-server &
celery -A workers.celery_app worker --loglevel=info

Training modes

The wizard supports three paths. Pick the mode in step 1 and the rest of the form adapts.

Mode What it does
SFT Supervised fine-tuning on instruction/chat data
DPO Preference alignment from chosen/rejected pairs
KD Knowledge distillation from a teacher model

Six adapter techniques are available per run: LoRA, QLoRA, AdaLoRA, IA3, prefix tuning, and prompt tuning. Advanced mode lets you stack a second technique on top via PeftMixedModel.

Supported models

Any Hugging Face causal LM works — target_modules are auto-detected. Well-tested defaults exist for Mistral 7B, Llama 3 8B, Phi-3 Mini, Gemma 2B, and Pythia 410M. Auto-detection also covers Qwen2/3, Falcon, StarCoder2, Mixtral, and more. See docs/supported-models.md for VRAM estimates.

Docs

Tests

poetry run pytest
poetry run ruff check .

The trainer integration test (real GPU, real model) is opt-in: TUNEOS_INTEGRATION_TESTS=1 pytest tests/test_trainer_integration.py.

Contributing

See CONTRIBUTING.md. Security issues go to SECURITY.md. Apache 2.0 license.

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Model operations workspace discover models, explore datasets, and fine tune with LoRA/QLoRA

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