bernice
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ettin-decoder-150m
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View details ↗ettin-decoder-17m
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View details ↗ettin-decoder-1b
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View details ↗ettin-decoder-32m
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View details ↗ettin-decoder-400m
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View details ↗ettin-decoder-68m
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View details ↗ettin-enc-from-dec-150m
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View details ↗ettin-enc-from-dec-1b
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View details ↗ettin-enc-from-dec-400m
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View details ↗ettin-encoder-150m
jhu-clsp> 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2. 📄 Paper | 🚀 GitHub Repository This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories.
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View details ↗ettin-encoder-17m
jhu-clsp> 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2. 📄 Paper | 🚀 GitHub Repository This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories.
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View details ↗ettin-encoder-1b
jhu-clsp> 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2. This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories. **Architecture-specific advantages persist**: A 400M encoder outperforms a 1B decoder on classification tasks, while a 400M decoder outperforms a 1B encoder on generation tasks.
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View details ↗ettin-encoder-32m
jhu-clsp> 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2. This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories. **Architecture-specific advantages persist**: A 400M encoder outperforms a 1B decoder on classification tasks, while a 400M decoder outperforms a 1B encoder on generation tasks.
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View details ↗ettin-encoder-400m
jhu-clsp> 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2. 📄 Paper | 🚀 GitHub Repository This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories.
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View details ↗ettin-encoder-68m
jhu-clsp> 🎯 **TL;DR**: State-of-the-art paired encoder and decoder models (17M-1B params) trained identically for fair comparison with open data. Encoders beat ModernBERT. Decoders beat Llama 3.2/SmolLM2. This model is part of the Ettin suite - the first collection of paired encoder-only and decoder-only models trained with identical data, architecture, and training recipes. Ettin enables fair comparisons between encoder and decoder architectures across multiple scales, providing state-of-the-art performance for open-data models in their respective size categories. **Architecture-specific advantages persist**: A 400M encoder outperforms a 1B decoder on classification tasks, while a 400M decoder outperforms a 1B encoder on generation tasks.
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View details ↗FollowIR-7B
jhu-clsp基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗mmBERT-base
jhu-clsp> TL;DR: A state-of-the-art multilingual encoder trained on 3T+ tokens across 1800+ languages, introducing novel techniques for learning low-resource languages during the decay phase. mmBERT is a modern multilingual encoder that significantly outperforms previous generation models like XLM-R on classification, embedding, and retrieval tasks. Built on the ModernBERT architecture with novel multilingual training innovations, mmBERT demonstrates that low-resource languages can be effectively learned during the decay phase of training. It is also significantly faster than any previous multilingual encoder. - Highlights - Quick Start - Model Description - Novel Training Innovations - Model Family - Training Data - Usage Examples - Fine-tuning Examples - Model Architecture - Citation
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View details ↗mmBERT-small
jhu-clsp> TL;DR: A state-of-the-art multilingual encoder trained on 3T+ tokens across 1800+ languages, introducing novel techniques for learning low-resource languages during the decay phase. mmBERT is a modern multilingual encoder that significantly outperforms previous generation models like XLM-R on classification, embedding, and retrieval tasks. Built on the ModernBERT architecture with novel multilingual training innovations, mmBERT demonstrates that low-resource languages can be effectively learned during the decay phase of training. It is also significantly faster than any previous multilingual encoder. - Highlights - Quick Start - Model Description - Novel Training Innovations - Model Family - Training Data - Usage Examples - Fine-tuning Examples - Model Architecture - Citation
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View details ↗rank1-7b
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