First time fine-tuning, need a sanity check — 3B or 7B for multi-task reasoning? [D]

**Reddit discussion thread where a self-taught practitioner asks for model selection advice before their first fine-tuning project targeting multi-task reasoning (subtext detection, perspective-holding, ambiguity handling).** No new research, benchmarks, or tooling is presented — this is a practitioner question seeking community guidance on choosing between a 3B and 7B parameter base model. The core tradeoff is relevant to others in similar positions: 3B models (e.g., Llama 3.2 3B, Phi-3.5 Mini) are cheaper to fine-tune and serve but may lack the representational capacity for complex, multi-step reasoning tasks, while 7B models (e.g., Mistral 7B, Llama 3.1 7B) generally show stronger baseline performance on reasoning benchmarks like MMLU and ARC but require more VRAM and compute. For practitioners advising beginners, the standard guidance is to start with a 7B if the task involves compositional reasoning and the hardware supports it (typically 16–24GB VRAM for QLoRA), since fine-tuning a 3B that under