omniASR-LLM-3B
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omniASR-LLM-7B
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View details ↗omniASR-LLM-7B-ZS
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View details ↗omniASR-W2V-1B
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View details ↗omniASR-W2V-300M
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View details ↗omniASR-W2V-3B
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View details ↗omniASR-W2V-7B
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View details ↗OMol25
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View details ↗opt-13b
facebookOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. Content from **this** model card has been written by the Hugging Face team. To quote the first two paragraphs of the official paper
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View details ↗opt-125m
facebookOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. Content from **this** model card has been written by the Hugging Face team. > Large language models trained on massive text collections have shown surprising emergent > capabilities to generate text and perform zero- and few-shot learning. While in some cases the public > can interact with these models through paid APIs, full model access is currently limited to only a > few highly resourced labs. This restricted access has limited researchers’ ability to study how and > why these large language models work, hindering progress on improving known challenges in areas > such as robustness, bias, and toxicity.
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View details ↗opt-2.7b
facebookOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. Content from **this** model card has been written by the Hugging Face team. To quote the first two paragraphs of the official paper
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View details ↗opt-30b
facebookOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. Content from **this** model card has been written by the Hugging Face team. > Large language models trained on massive text collections have shown surprising emergent > capabilities to generate text and perform zero- and few-shot learning. While in some cases the public > can interact with these models through paid APIs, full model access is currently limited to only a > few highly resourced labs. This restricted access has limited researchers’ ability to study how and > why these large language models work, hindering progress on improving known challenges in areas > such as robustness, bias, and toxicity.
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View details ↗opt-350m
facebookOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. Content from **this** model card has been written by the Hugging Face team. To quote the first two paragraphs of the official paper
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View details ↗opt-6.7b
facebookOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. Content from **this** model card has been written by the Hugging Face team. To quote the first two paragraphs of the official paper
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View details ↗opt-66b
facebookOPT was first introduced in Open Pre-trained Transformer Language Models and first released in metaseq's repository on May 3rd 2022 by Meta AI. **Disclaimer**: The team releasing OPT wrote an official model card, which is available in Appendix D of the paper. Content from **this** model card has been written by the Hugging Face team. > Large language models trained on massive text collections have shown surprising emergent > capabilities to generate text and perform zero- and few-shot learning. While in some cases the public > can interact with these models through paid APIs, full model access is currently limited to only a > few highly resourced labs. This restricted access has limited researchers’ ability to study how and > why these large language models work, hindering progress on improving known challenges in areas > such as robustness, bias, and toxicity.
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View details ↗opt-iml-1.3b
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View details ↗opt-iml-30b
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View details ↗opt-iml-max-1.3b
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View details ↗opt-iml-max-30b
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View details ↗oss_gate_test
facebook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 ↗pe-a-frame-base
facebookPE-A-Frame is a state-of-the-art audio-text embedding model. For text, the model produces a single embedding. For audio, it produces a sequence of embeddings (onen for every 40ms of audio). These embeddings can then be used for audio event localization. For convienience, model outputs temporal spans (start and end timestamps) indicating when that event (freeform audio description) occurs in the audio. PE-A-Frame uses contrastive learning to align frame-level audio representations with text descriptions. The model can identify precise time ranges when described audio events occur The `threshold` parameter controls sensitivity for event detection. Lower values detect more events (higher recall), while higher values are more selective (higher precision):
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View details ↗pe-a-frame-large
facebookPE-A-Frame is a state-of-the-art audio-text embedding model. For text, the model produces a single embedding. For audio, it produces a sequence of embeddings (onen for every 40ms of audio). These embeddings can then be used for audio event localization. For convienience, model outputs temporal spans (start and end timestamps) indicating when that event (freeform audio description) occurs in the audio. PE-A-Frame uses contrastive learning to align frame-level audio representations with text descriptions. The model can identify precise time ranges when described audio events occur The `threshold` parameter controls sensitivity for event detection. Lower values detect more events (higher recall), while higher values are more selective (higher precision):
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View details ↗pe-a-frame-small
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View details ↗pe-av-base
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