Comment by StilesCrisis
2 hours ago
> You don't need bigger or better models. Current models are more than adequate for any task.
At the very least we need _current_ models, though. The weights have all sorts of information baked into them, like "how do Python unit test drivers work" or "what does idiomatic C# look like," that will just naturally go stale over time as the frameworks improve. In five years, Qwen 3.8 will still be targeting the Python of 2026.
Not really - fine tuning can be done on much lower tier hardware, with much smaller datasets. With a model like Qwen 3.8 27B as the core, a small fleet of LoRA adapters for whatever specific use cases you need would be incredibly competitive with the current frontier. Sure, future frontier models will likely continue to improve, but will people need them for everyday usage? I'd wager not. Unfortunately, my wager is dwarfed by OpenAI's and Anthropic's.
Can you just keep fine-tuning indefinitely? I don't really know but it feels like eventually that might start impinging on the model's abilities?