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Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It is designed to keep track of information across extended tasks, work through...
Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and coding workflows. It is designed to track information...
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The ONNX file is `RitualCNN.onnx`. It accepts a grayscale image as a `float32` tensor named `input`, with shape `[batch, 1, 128, 128]`. Before inference, convert the image to grayscale, resize it to 128 by 128 pixels, set pixels above 127 to 1 and all other pixels to 0. The model returns seven raw scores named `output`. The highest score is the predicted class. These are model inputs from the held-out validation set after preprocessing. When a class has no correct validation predictions, its row shows the highest-scoring example. The top softmax score is not a calibrated probability.
KyrgyzBert is a **small-scale BERT-based language model** pre-trained on a large **Kyrgyz text corpus**. It is designed for **masked language modeling (MLM), text classification, and Kyrgyz NLP applications**. Developed by **Metinov Adilet**, this model aims to enhance Kyrgyz NLP research and practical applications. This model was trained on non-disclosable dataset containing over 1.5 million sentences. The dataset was tokenized using a **metinovadilet/bert-kyrgyz-tokenizer**. This model was developed by **Metinov Adilet**. If you use this model, please consider citing our work.
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