Blog & Demos

Tutorials, case studies, benchmarks, and open-source demos — everything you need to build with small language models.

When Does Reinforcement Learning Help Small Language Models?
BenchmarkClassificationQuestion AnsweringTool Calling

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.
GuideTool CallingOn-Prem / Edge

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
DemoOn-Prem / Edge

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
Case StudyOn-Prem / EdgeTool Calling

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
Case StudyAgentic AIOn-Prem / Edge

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
GuideClassification

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
DemoClassificationOn-Prem / Edge

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.

Train Your SLM with the distil labs Claude Skill
GuideQuestion Answering

Train Your SLM with the distil labs Claude Skill

A step-by-step walkthrough of training a Text2SQL small language model using the distil labs Claude Code skill, going from raw conversation data to a working local model in a single conversation.

DemoQuestion AnsweringOn-Prem / Edge

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.