Asking the right question before sinking weeks into a project is admirable. They're not debating whether fine-tuning is necessary, they've already hit the ceiling that prompt engineering inevitably reaches. Instead, they're wrestling with model size, data scale, and whether three related but distinct reasoning tasks can live together in a small model without collapsing into mush. That's the kind of foresight that separates a smooth first project from a frustrating one.
Here's our honest read: for what they're trying to do, a 3B model is likely to be a frustrating bottleneck, and not because it can't learn the tasks. It can. But the three skills they've described, reading beneath a question, holding multiple perspectives, and identifying the load-bearing thread in messy input, are not three separate tricks. They're three expressions of the same underlying capacity for interpretive reasoning. And that capacity is exactly what small models tend to handle unevenly when the training distribution shifts. The author even senses this: they ask whether "related but not identical" makes training harder. Yes, it does. A 3B model will happily memorize patterns from 50,000 examples, but when the input is novel, it often latches onto surface features instead of the deeper structure. That's not a failure of effort; it's a limitation of capacity. The 7B model, especially Qwen 2.5, has more room to generalize those reasoning modes without confusing them.
That said, the hardware constraint is real. A 24GB M4 Mac is genuinely workable for 7B with LoRA, but it's tight, and keeping GPU rental on the table is the smart move. The smarter move might be to start with 7B on rented hardware, validate whether the three tasks actually co-train well, and then decide if a smaller model is worth the trade-off for local inference. What they should not do is assume that more data fixes everything. With 40-60k examples, quality and diversity of the synthetic data will matter far more than the raw count. A 3B model trained on 60k well-structured examples will outperform a 7B model trained on 40k sloppy ones. So the real question isn't just 3B or 7B, it's whether they can generate data that teaches the model to distinguish between the surface question and the underlying concern, and then to hold that tension without collapsing.
The thing that will bite them isn't model size. It's the assumption that these three tasks are naturally compatible because they feel related. They're not. Reading beneath a question is a classification problem. Holding multiple perspectives is a generation problem. Identifying the load-bearing thread is a ranking problem. Training a single model to do all three at once means the gradients are pulling in different directions, and small models are less forgiving of that tension. The author should expect to see the model excel at one task while regressing on another, especially on out-of-distribution inputs. That's not a bug in their approach; it's the reality of multi-task fine-tuning at this scale. The practical takeaway: start with 7B, rent the GPU, and build a small evaluation set that tests each of the three skills separately before running the full training run. If the 7B can't handle all three well, no amount of 3B optimization will save them.