Unsloth docs guide local fine-tuning

In that framing, Unsloth is the right answer: the hard part is less the training command and more choosing the right model, chat template, and dataset shape.

• –The docs explicitly walk through beginner questions like model choice, dataset size, dataset structure, and deployment.
• –The datasets guide splits training data into raw corpus, instruct, conversation, and RL-style formats, which is more useful than treating JSONL as the goal itself.
• –For smaller SaaS-specific datasets, starting with an instruct model is usually safer than forcing a base-model fine-tune from day one.
• –The beginner guide also points people to QA, installation, inference/deployment, and hyperparameters, which makes it a decent end-to-end curriculum.
• –In practice, MCP belongs in the post-training tool layer for edge cases and workflows, not as a replacement for a well-curated training set.