Glossary
Plain-English definitions for fine-tuning concepts
Resources
Everything we know about fine-tuning, in one place.
Start here
Learn the foundations
Pick a model size
Match model size to your data and hardware
Tool roundups
Curated best-of guides across the AI stack
Choose your approach
Compare your options
Compare to alternatives
How Studio stacks up against the LLM APIs you might be using
Technique and tool comparisons
Side-by-side decisions that come up in fine-tuning
Data formats
JSONL, parquet, and the formats Ertas accepts
Build something real
Roll up your sleeves
On-Device Cookbook
A complete walkthrough from raw dataset to GGUF in production
Dataset templates
Pre-formatted JSONL templates for common tasks
Models reference
Quick reference for popular open-source base models
Connect your stack
Integrations
Coding assistants & IDEs
Wire your fine-tuned model into the tools you code in
Agent & app frameworks
Plug Ertas models into your agents and LLM apps
Local runtimes
Run your GGUF on-device with popular local engines
Free downloads
Cheat sheets and templates
On-device AI economics cheat sheet
The cost curve of cloud APIs against on-device inference, on one page
Dataset templates pack
Pre-formatted JSONL for the tasks people fine-tune first
Ship your first on-device model this weekend
A free email course from raw data to a model running in your app
Read and learn
Go deeper
Latest from the blog
What we're publishing this month
Documentation
The full Studio reference for APIs, integrations, and advanced topics
Research
Papers and technical reports from the Ertas AI team, with the code and models behind them