@QingQ77: Training a 0.1B end-to-end omnimodal model from scratch. A single set of weights handles text, speech, and image inputs, while outputting text and streaming speech. https://github.com/jingyaogong/minimind-o… MiniMind-O is an omnimodal model with only 0.1B parameters…

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MiniMind-O has released an end-to-end omnimodal model with only 0.1B parameters, supporting text, speech, and image inputs as well as streaming speech output. The project opensources the code, weights, training data, and technical report, emphasizing that both training and inference can be performed quickly on standard GPUs.

Training a 0.1B end-to-end omnimodal model from scratch. A single set of weights handles text, speech, and image inputs, while outputting text and streaming speech. https://github.com/jingyaogong/minimind-o… MiniMind-O is an omnimodal model with only 0.1B parameters, featuring a Thinker-Talker dual-path design. It supports text, speech, and image inputs, and outputs text and streaming speech. This project opensources the code, weights, training data, and technical report. The core algorithm was written from scratch in PyTorch, allowing the mini dataset training to be completed in just two hours on a single RTX 3090.
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