Blog & Demos
Tutorials, case studies, benchmarks, and open-source demos – everything you need to build with small language models.
How SLMs Can Enable On-Device RAG - Making Industrial Machinery More Usable
Fine-tuned 1B parameter models can match the accuracy of 3B base models on domain-specific documentation, making on-device RAG viable for industrial equipment without expensive AI-optimized hardware. We tested this on a Siemens PLC manual and achieved a +16 percentage point accuracy gain through distillation.
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.
When Does Reinforcement Learning Help Small Language Models?
A controlled experiment across 12 datasets reveals that adding RLVR after SFT consistently improves text generation tasks (+2.0pp) but provides no reliable benefit for structured tasks like classification and function calling.
The LLM in Your Voice Assistant Is the Latency Bottleneck. Replace It with an SLM.
Voice assistants on cloud LLMs are slow and expensive per turn. A fine-tuned SLM is cheaper and faster per request with equal-or-better accuracy on bounded tasks: brain-stage latency drops from ~700ms to ~40ms, and per-turn cost from cloud-API rates to server-amortized pennies.
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.
Helping Rocketgraph's customers with an OpenCypher-specialized small language model
How distil labs partnered with Rocketgraph to finetune a small language model specialized in translating user questions to Rocketgraph-compliant Cypher queries on IBM Power hardware.
Teaching Small Language Models New Skills - Training a Local Cybersecurity Agent
How distil labs partnered with Octodet to train a small language model that outperforms LLMs 30x its size at analyzing cybersecurity logs, while running entirely on-premises to meet strict privacy requirements.
Vibe-Tuning: The Art of Fine-Tuning Small Language Models with a Prompt
Fine-tuning is a pain – you need datasets, ML expertise, and a stack of GPUs just to get started. Not anymore. With model vibe-tuning, you go from prompt to production-ready model without these headaches. This blog post shows you exactly how to build one, starting with just a prompt.
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.