We trained saas-business-strategist-agent today: a small business and SaaS strategy model, trained on a CPU from a list of links, and published it on Hugging Face. You can train your own the same way.
Model: https://huggingface.co/profullstack/saas-business-strategist-agent
The c0mpute example takes links and turns them into a training corpus:
No API keys. rssamplifier and pit.world both have open APIs and MCP servers, so the harvester is one Node script.
node harvest.mjs https://rssamplifier.com/topics/business,saas,crypto \
'https://pit.world/search?q=saas,business,crypto' --full --out data/saas-business.jsonl
c0mpute 0.2.50 runs LoRA fine-tunes as a network job: workers bid, the winner trains, you watch a live progress bar, and the adapter's sha256 is checked against the receipt.
c0mpute infernet text train --job job-saas.json --dataset data/saas-business.jsonl \
--output out/saas-business-strategist-agent --yes
Or locally:
.venv/bin/python train.py job.json --name saas-business-strategist-agent --dataset data/saas-business.jsonl
The first version used a 135M model. It echoed questions back. 0.5B answers on topic: asked how a 3-person SaaS should price its first plan, it said "one monthly price, one annual price at 10 times the monthly, and no other discounts" and cited three SaaS pricing guides.
A sub-1B model learns the voice, not the facts. So the agent searches pit.world for each question and puts the top pages in the prompt. The facts come from search and the tune supplies the register.
The example, with the harvester, trainer, chat and Hugging Face publisher: https://github.com/profullstack/c0mpute/tree/master/examples/business-strategist-agent
Swap the links for your own topics and give it a name.