gemma-scope-2-4b-pt
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gemma-scope-27b-pt-res
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View details ↗gemma-scope-2b-pt-att
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View details ↗gemma-scope-2b-pt-mlp
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View details ↗gemma-scope-2b-pt-res
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View details ↗gemma-scope-9b-it-res
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View details ↗gemma-scope-9b-pt-att
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View details ↗gemma-scope-9b-pt-mlp
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View details ↗gemma-scope-9b-pt-res
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View details ↗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 ↗gnm-v3
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View details ↗hear
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View details ↗hear-pytorch
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View details ↗long-t5-local-base
GoogleLongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be found in Flaxformer repository which uses another Google research project repository T5x. Disclaimer: The team releasing LongT5 did not write a model card for this model so this model card has been written by the Hugging Face team. LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting (Pegasus-like generation pre-training). LongT5 model is an extension of T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The usage of attention sparsity patterns allows the model to efficiently handle input sequence.
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View details ↗long-t5-local-large
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View details ↗long-t5-tglobal-base
GoogleLongT5 model pre-trained on English language. The model was introduced in the paper LongT5: Efficient Text-To-Text Transformer for Long Sequences by Guo et al. and first released in the LongT5 repository. All the model architecture and configuration can be found in Flaxformer repository which uses another Google research project repository T5x. Disclaimer: The team releasing LongT5 did not write a model card for this model so this model card has been written by the Hugging Face team. LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting (Pegasus-like generation pre-training). LongT5 model is an extension of T5 model, and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The usage of attention sparsity patterns allows the model to efficiently handle input sequence.
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View details ↗long-t5-tglobal-large
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View details ↗long-t5-tglobal-xl
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View details ↗Lyria 3 Clip Preview
Google30 second duration clips are priced at $0.04 per clip. Lyria 3 is Google's family of music generation models, available through the Gemini API. With Lyria 3, you can generate...
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$0 / M input tokensOpenRouter · google/lyria-3-clip-preview
View details ↗Lyria 3 Pro Preview
GoogleFull-length songs are priced at $0.08 per song. Lyria 3 is Google's family of music generation models, available through the Gemini API. With Lyria 3, you can generate high-quality, 48kHz...
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$0 / M input tokensOpenRouter · google/lyria-3-pro-preview
View details ↗madlad400-10b-mt
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View details ↗madlad400-3b-mt
Google0. TL;DR 1. Model Details 2. Usage 3. Uses 4. Bias, Risks, and Limitations 5. Training Details 6. Evaluation 7. Environmental Impact 8. Citation MADLAD-400-3B-MT is a multilingual machine translation model based on the T5 architecture that was trained on 1 trillion tokens covering over 450 languages using publicly available data. It is competitive with models that are significantly larger. **Disclaimer**: Juarez Bochi, who was not involved in this research, converted the original weights and wrote the contents of this model card based on the original paper and Flan-T5.
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View details ↗madlad400-7b-mt
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