End-2-end tutorial on fine-tuning, the whole journey

Reddit r/LocalLLaMA / 4/28/2026

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Key Points

The article shares a hands-on end-to-end tutorial that walks through the full fine-tuning journey from problem framing to model fine-tuning and evaluation.
As a concrete example, it builds a wildfire prevention system using satellite images and a small vision-language model (LFM2.5-VL-450M) to identify risk factors linked to wildfire probability.
The tutorial is structured around key phases including problem framing, system design, evaluation, and the fine-tuning process.
It links to an example implementation for the wildfire prevention workflow, intended to help practitioners reproduce the approach.

End-2-end tutorial on fine-tuning, the whole journey

I put together a hands-on tutorial that takes you from problem framing to fine-tuning, step by step.

I decided to build a wildfire prevention system that uses satellite images and a Small Vision-Language Model (LFM2.5-VL-450M) to extract relevant risk factors that correlate with wildfire probability.

The whole journey is covered:

- Problem framing

- System design

- Evaluation

- Fine-tuning

I hope this helps :-)

submitted by /u/PauLabartaBajo
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