Build custom AI chatbots and assistants without writing code. Collect training data from the web or your own documents, teach AI models on your computer or in the cloud, and test the results by chatting with your creation. Everything happens in one easy-to-use desktop app.
A free desktop app that makes AI customization accessible to everyone — no coding or cloud subscriptions required
finefoundry.db) is now the sole storage mechanism for all application dataapp_logs table with queryable accessff_settings.json — Settings now in databasesrc/saved_configs/ — Training configs now in databaselogs/ directory — Logs now in database--verbose flag for detailed debug output during generation--config flag to load options from YAML config files--keep-server flag for model caching between batch runs (10x faster subsequent runs)FineFoundry brings together data collection, dataset building, model training, and inference into a single desktop app. No more juggling scripts and notebooks—just open the app, collect your data, train your model, and test it.
From raw data to trained model, all in one workflow
Scrape conversations from 4chan, Reddit, and Stack Exchange, or generate Q&A pairs from your own PDFs and documents using local LLMs.
Combine data from multiple sources into unified datasets. FineFoundry handles column mapping and filtering automatically.
Check your data before training—find duplicates, detect toxicity, measure sentiment, and catch data leakage between splits.
Build train/val/test splits and push directly to Hugging Face with auto-generated dataset cards. Share your work with the community.
Fine-tune models on your own computer or rent cloud GPUs from RunPod. Start small and scale up when you're ready.
Run inference against your fine-tuned adapters right in the app. Chat with your model and see how it performs on real prompts.
Run standardized benchmarks (HellaSwag, TruthfulQA, MMLU, etc.) to measure model performance with real numbers, not just gut feeling.
Built with Flet for cross-platform native UI
LoRA fine-tuning with PyTorch, Transformers, PEFT, bitsandbytes
Seamless Hugging Face Hub push and pull
Automated pod and network volume management
All processing happens locally on your machine
Quote-chain, cumulative, and adjacent modes
Use as GUI or programmatic API
Open source and free to use
Guides to help you go from installation to your first trained model
Clone the repo and run with uv, or use a classic virtualenv.
# Recommended: uv (matches project docs)
git clone https://github.com/SourceBox-LLC/FineFoundry.git FineFoundry-Core
cd FineFoundry-Core
# Install uv if needed
pip install uv
# Run the app (creates an isolated env and installs deps)
uv run src/main.py
# Alternative: classic venv + pip
python -m venv venv
# Activate (Windows PowerShell)
./venv/Scripts/Activate.ps1
# Activate (macOS/Linux)
source venv/bin/activate
# Install dependencies
pip install -e .Start the desktop application
# If using uv (recommended)
uv run src/main.py
# If using a virtualenv + pip
python src/main.py
# Or use Flet directly
flet run src/main.pyThe desktop window will open with tabs for Data Sources, Dataset Analysis, Merge Datasets, Training, Inference, Publish, and Settings.
Collect conversational training data from multiple sources with configurable pairing modes.
Multi-board scraping with quote-chain and cumulative pairing
Subreddits or single posts with parent-child threading
Q&A pairs from accepted answers
Generate Q&A, CoT, or summaries from PDFs/docs using local LLMs
Publish datasets and LoRA adapters (Phase 1) directly to Hugging Face Hub.
username/my-dataset)Fine-tune LLMs using an Unsloth-based LoRA training stack on RunPod or locally via Docker.
Cloud GPU training with automated pod and network volume management
Train on your local GPU using the same Unsloth trainer image
Both targets use docker.io/sbussiso/unsloth-trainer:latest with:
Run local inference against adapters from completed training runs with prompt history and Full Chat View.
Powered by the same stack as training:
AutoModelForCausalLM, AutoTokenizer)PeftModel) for adapter loadingSystematically benchmark your fine-tuned models using standardized tests from EleutherAI's lm-evaluation-harness — the same framework that powers the HuggingFace Open LLM Leaderboard.
Combine multiple database sessions (and optionally Hugging Face datasets when online) into a unified training set.
Comprehensive quality analysis with togglable modules for different metrics.
Automate FineFoundry workflows with command-line tools and Python APIs.
uv run src/scrapers/reddit_scraper.py \
--url https://www.reddit.com/r/AskReddit/ \
--max-posts 50 \
--mode contextual \
--pairs-path reddit_pairs.jsonuv run src/save_dataset.pyfrom src.scrapers.fourchan_scraper import scrape
pairs = scrape(
board="pol",
max_threads=150,
max_pairs=5000,
mode="contextual",
strategy="cumulative"
)Run training jobs in containers on RunPod or locally.
Default image: docker.io/sbussiso/unsloth-trainer:latest
Cloud GPU training with automated infrastructure
/data/outputs/...Configure authentication, proxies, integrations, and run a built-in System Check diagnostics panel.
finefoundry.db) and never sent to external servers.
Join the FineFoundry community
FineFoundry is open source and welcomes contributions! Whether you're adding new scrapers, improving analysis modules, enhancing the UI, or fixing bugs, your input is valuable.
Python 3.10+
Flet
Datasets (HF)
Docker
PyTorch
RunPod
Hugging Face Hub
REST APIs
Unsloth
Transformers
PEFT / LoRA
bitsandbytes
SyntheticDataKit
vLLM
SQLite