If you don’t mind me giving you a suggestion. An easy way to get started is by using Unsloth’s Google Colab notebooks. Just by inspecting the code of some of their many notebooks you can get a solid starting point about the fine-tunneling steps, including the dataset formats.

Unsloth - Open source Fine-tuning & RL for LLMs

Unsloth AI - Open Source Fine-tuning & RL for LLMs

Open source fine-tuning & reinforcment learning (RL) for gpt-oss, Llama 4, DeepSeek-R1, Gemma, and Qwen3 LLMs! Beginner friendly.

Thank you I'll give this a test

Download all the notes. Take the "content" field from the notes and change the name to "text": Previously: {"id":".....................", "pubkey": ".................", "content": "gm, pv, bitcoin fixes this!", .......} {"id":".....................", "pubkey": ".................", "content": "second note", .......} Converted into jsonl file: {"text": "gm, pv, bitcoin fixes this!" } {"text": "second note" } Used Unsloth and ms-swift to train. Unsloth needed to convert from base to instruct. This is a little advanced. If you don't want to do that and just start with instruct model, you can use ms-swift or llama-factory. You will do lora, pretraining. I used 32 as lora rank but you can choose another number.

Excellent I figured that was structure. Thank you for the detailed information