Demos
Open-source projects you can clone, run, and learn from.
Giving Superpowers to Your LLMs with SLMs
Part 2 of the autonomous bug-fixing agent series: why the intelligent harness keeps the LLM orchestrator general-purpose and offloads domain work to cheap, fine-tuned SLM tools — cutting cost from ~$0.07 to ~$0.001 per incident.
Autonomous Bug Fixing Agent with distil labs' SLM and Warp Oz
A self-healing loop that diagnoses production failures with a fine-tuned 0.6B SLM and applies the fix with Warp Oz — closing incidents in seconds, no humans paged.
How to label your emails locally with a distil labs fine-tuned model and n8n
Build a fully local Gmail email classification pipeline using a distil labs fine-tuned 0.6B model and n8n, keeping all email data private on your machine.
Making FunctionGemma Work: Multi-Turn Tool Calling at 270M Parameters
Google's FunctionGemma scores just 10-39% on multi-turn tool calling out of the box, but after fine-tuning with distil labs it reaches 90-97% accuracy across three benchmarks, matching or exceeding a 120B teacher model at 270M parameters.
pytest-generator: AI-Powered Unit Test Generation
Generate high-quality pytest test cases from Python function signatures and docstrings. Runs entirely on your local machine with zero API costs and complete privacy.
AI Slop Detector: Catch AI-generated text with a 270M model that runs in your browser
A fine-tuned 270M parameter model that detects AI-generated text entirely in your browser — no API keys, no cloud, no data leakage. Matches 120B teacher accuracy at 400x smaller size.
Text2SQL: Natural Language CSV Query Tool
Query your CSV data using natural language questions. A fine-tuned 0.6B parameter model converts questions into SQL queries and executes them locally — no cloud required.
SHELLper: Multi-turn Bash Function Calling Model
A fine-tuned 0.6B model that converts natural language into bash commands with 100% multi-turn tool call accuracy. Runs locally with full privacy.
Text2SQirreL 🐿️: Query your data in plain English
A fine-tuned small language model that converts plain English questions into executable SQL queries. Runs locally with no API keys, no cloud dependencies, and full privacy — matching the accuracy of a 685B teacher model.