A DeepSeek-R1 variant was fine tuned for smart contract vulnerability detection using GRPO and LoRA on a single A100 80GB. The result is a task specific model optimized for screening, not full audits.

Key outcomes:

strong improvement in vulnerable vs clean classification

reliable structured outputs, better integration into pipelines

weak performance in deep vulnerability reasoning, SWC identification remains limited

The model learned formatting faster than security understanding. Usability improved before trustworthiness.

Role of Decentralized Compute

@Fluence enabled the entire training run at a cost of $30.97.

This is the shift:

GPU access without centralized cloud pricing

on demand compute sourced from distributed providers

low cost experimentation with specialized models

Outcome

Instead of expensive general models, developers can iterate on cheap, targeted systems for specific tasks like first pass contract screening.

Fluence reduces compute cost.

Lower compute cost enables rapid, niche AI development.