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.

We trained and released a family of small language models (SLMs) specialized for policy-aware PII redaction. After targeted fine-tuning on a compact, well-specified task, our SLMs dramatically outperform their pre-trained counterparts on an LLM-as-judge evaluation. Notably, the **1B model-which can be deployed on a laptop-achieves 0.81 ± 0.02, effectively matching a frontier 600B+ LLM class (e.g., DeepSeek 3.1 at 0.84 ± 0.03)** while retaining tight latency, cost, and on-device privacy.

In this blog, we discuss how we fine-tuned a small language model to generate OpenCypher queries for the Rocketgraph analytics platform, thereby enabling accurate, efficient and privacy-first AI-powered natural language querying capabilities for Rocketgraph's customers.

How we created a local tool-calling language model to turn plain-English language questions into git commands with the accuracy of a cloud LLM. You can check it out in our GitHub repo or get the model directly from Huggingface.

In this post we explore how you can combine the best of both worlds: speed of development of LLMs but control and efficiency machine learning. This blueprint offers a more efficient and environmentally sustainable way of designing AI systems. Use SLMs for narrow agent tasks: faster, cheaper, private. Keep LLMs for open-ended work.

In this post we benchmark our distilled SLMs against a strong teacher across classification, extraction, QA (open + closed), and tool-calling. The punchline: trained students crush baselines and match or beat the teacher accuracy.