🔁 Teaching a 15M French LLM to think deeper — and to know when to stop 🇫🇷

In this exploration of refining a 15M parameter French language model, the author recounts the iterative process of enhancing model quality by focusing on computation adjustments rather than scaling parameters. The journey began with several unsuccessful attempts to modify learning dynamics, which revealed that model capacity was the limiting factor rather than the learning process itself. The breakthrough came from implementing a looped transformer architecture, allowing the model to use the same block multiple times, which improved perplexity and coherence. This approach was inspired by existing concepts like the Recurrent-Depth Transformer and Adaptive Computation Time but was adapted for language processing with a novel, parameter-free entropy-based halting mechanism during training. Although these adjustments did not increase the model's factual knowledge, they enhanced the compositionality and coherence of its outputs, particularly in domain-specific contexts, illustrating that improvements in model architecture can lead to qualitative gains without increasing parameter count. The author emphasizes the importance of thorough testing and validation to ensure reliable results, noting that multi-seed validation is the next step to solidify these preliminary findings.

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