MiMo-Audio - Audio language models are few-shot learners
Xiaomi's MiMo-Audio is a breakthrough in open-source audio intelligence. Pre-trained on over 100M hours of data, it's the first audio model to show emergent few-shot generalization and In-Context Learning.
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Flowtica Scribe
Hi everyone!
Five years ago, GPT-3 kicked off a new era for LLMs, proving that few-shot generalization was possible at scale. The audio domain, however, has largely been stuck, limited by its reliance on massive labeled datasets.
Today, Xiaomi's MiMo-Audio is changing that. Based on a new pre-training architecture and over 100 million hours of data, we're seeing true "emergence" and In-Context Learning capabilities in an open-source audio model for the first time.
More importantly, they've open-sourced the entire stack: the tokenizer, the new model architecture, the training methods, and the evaluation suite. It makes you wonder: is this the "LLaMA moment" for open-source audio models?
You can experience this audio model here.
Backender.io
This is the type of progress that reminds me why audio AI is so fascinating. It reaching emergent few-shot learning is massive and open-sourcing means the community benefits directly.