Model training by Maincode, the team behind Matilda.
Maincode is an Australian owned and operated AI company, made in Melbourne. We built Matilda Train to train our own models. Now your team can train on it too, with your data kept in Australia.
As used by
Built to train our own models
Maincode built this platform to train its own models. The scheduler, the isolation, the run records and the agent tools are the ones our researchers use every day, so the problems that slow training down have already been dealt with.
When you start, a Maincode data scientist works with your team to port your code and tune the first runs. You work with people who train models for a living.
- Company
- Maincode, an Australian AI company and the maker of Matilda
- Where your data lives
- Australian infrastructure that Maincode operates
- Who can see your files
- Not other users. Being in the same organisation doesn’t give anyone access to your runs or files.
- Support
- A data scientist from day one, included in the price
From a prompt to your own weights, in three steps
No console to learn and no cluster to run. If you can ask your coding agent for a change, you can ask it for a model.
- Step 01
Connect your agent
Add one remote MCP server to Matilda Code, or any MCP-capable coding agent or IDE, then approve the connection in your browser. Any client that supports remote MCP over HTTP with OAuth works.
https://train.matilda.com/mcp/research - Step 02
Ask for the model you want
Describe the run in plain language. Your agent stages the data, checks the request against your limits with research_validate, then submits a durable job that keeps running if you close the laptop.
Fine-tune Qwen3-8B on data/tickets.jsonl. Hold out 5% for eval, use 8 GPUs, and stop when eval loss stops improving. - Step 03
Take the weights
Checkpoints and adapters land in your workspace. Download them through your agent, SSH or the HTTP API, and serve them anywhere you like.
runs/4f19a2/ ├─ model/ │ ├─ config.json │ └─ model-00001-of-00004.safetensors ├─ metrics.jsonl └─ run-record.json
Bring your training.
Own the model.
Create your account, add compute credits and connect your coding agent. You pay by the GPU-hour, and every checkpoint is yours to download.