Train a business strategy model from links, on c0mpute, and publish it to Hugging Face

A developer trained and published saas-business-strategist-agent, a small business and SaaS strategy model, on Hugging Face using c0mpute 0.2.50, which runs LoRA fine-tunes as a network job where workers bid and the winning adapter's sha256 is verified against the receipt. The pipeline harvests training data from a list of links via the open APIs and MCP servers of rssamplifier and pit.world, requiring no API keys. A first 135M-parameter version echoed questions back, while a 0.5B version answered on topic, and because sub-1B models learn voice rather than facts, the agent searches pit.world per question and places top pages in the prompt.

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 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 https://github.com/profullstack/c0mpute/tree/master/examples/business-strategist-agent Swap the links for your own topics and give it a name.