Episode Details

Daniel in Jerusalem has two NVIDIA Parakeet fine-tunes — one for Hebrew-English code-switching, one for niche technical vocabulary — and wants to know if he can merge them like LEGO bricks instead of retraining on a combined dataset. It's a question with a combinatorial edge: hospitals, legal teams, and multilingual clinics all want pairings nobody will ever build dedicated models for. We walk through what merging actually does to weights — task vectors, weight averaging, TIES-Merging, DARE, and LoRA adapter merging — and ask whether transcription's demand for exactness makes unified training the only honest path. Along the way: why word error rate can hide the exact failures you fine-tuned to fix, and why fine-tunes from the same base checkpoint come pre-aligned. Key sources: • TIES-Merging: Resolving Interference When Merging Models — https://arxiv.org/html/2306.01708v2 • An Introduction to Model Merging for LLMs — https://developer.nvidia.com/blog/an-introduction-to-model-merging-for-llms/ • NeMo Speech Fine-Tuning docs — https://docs.nvidia.com/nemo/speech/latest/asr/fine_tuning.html Episode 6027 — myweirdprompts.com/n/6027 (permanent short link: myweirdprompts.com/991040)