w2.2
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Lythri-4B-A2B
Lythri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 ↗Lythri-4B-A2B-GGUF
Lythri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 ↗Lythri-7B-A4B
Lythri**Lythri** is a family of on-device language models built for emotional companionship. Instead of chasing math and coding scores, Lythri is trained to understand how people feel and to hold natural, multi-turn conversations, while staying small enough to run locally on a laptop or phone. Zero-shot comparison between Lythri-4B-A2B and Gemma 4 instruction-tuned baselines on three emotion benchmarks. Despite having only 2.3B active parameters, **Lythri-4B-A2B(4.6B, base on Gemma4-E2B-PT) comparable to Gemma4-E4B-IT(8B) on all three EQ benchmarks.** All benchmarks are evaluated with their official standard settings and in generative mode with chat template applied, reflecting real-world inference conditions. Think-tag outputs from model are stripped before answer extraction.
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View details ↗Lythri-7B-A4B-GGUF
LythriThis repository contains GGUF quantizations of Lythri-7B-A4B for use with llama.cpp and compatible apps. For model details, evaluation results and limitations, see the original model card. > **Note:** Lythri models are highly sensitive to quantization. We recommend using the highest-precision quantization your hardware allows. Lower-bit quantizations still work but are not recommended. To use a specific quantization, append its type to the repository name, for example `Lythri/Lythri-7B-A4B-GGUF:Q8_0`.
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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 ↗mbart50-tradenewssum
lyutovad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 ↗DreamShaper
lzyvegetable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 ↗FLUX.1-schnell
lzyvegetable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 ↗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 ↗MERT-v1-330M
m-a-pThe development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with **MLM** paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with **BYOL** paradigm. The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. - **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. - **Feature Rate**: Given a 1-second audio input, the number of features output by the model. - **Sample Rate**: The frequency of audio that the model is trained with.
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View details ↗MERT-v1-95M
m-a-pThe development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with **MLM** paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with **BYOL** paradigm. The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. - **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. - **Feature Rate**: Given a 1-second audio input, the number of features output by the model. - **Sample Rate**: The frequency of audio that the model is trained with.
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View details ↗MERT-v2-30s
m-a-p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 ↗MERT-v2-FullSong
m-a-p🤗 MERT-v2-FullSong Music representations with full-song context 24 kHz mono · 632M parameters · 24 layers · 1,024 dimensions · 25 Hz 🎵 YuE2 project · 🚀 Quick start · 📊 MARBLE · 🎛️ Layer guide · 📄 arXiv · PDF · 📚 Citation **MERT-v2-FullSong is a bidirectional music encoder adapted to complete songs lasting 30–360 seconds.** Extract general-purpose music representations at the frame or recording level. Load with standard Hugging Face Transformers, using the familiar MERT workflow.
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View details ↗MIO-7B-Base
m-a-p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 ↗MIO-7B-Instruct
m-a-p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 ↗MuPT-v0-4096-1.97B
m-a-p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 ↗music2vec-v1
m-a-p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 ↗OProver-8B
m-a-p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 ↗SheetSage2
m-a-p🤗 SheetSage2 Music audio to editable scores Melody · Chords · Beats · Key · Structure 🎵 YuE2 project · 💻 Code · 🚀 Quick start · 📊 Benchmarks · 📄 SheetSage2 arXiv · 📄 YuE2 arXiv · YuE2 PDF · 📚 Citation **SheetSage2 turns music recordings into lead sheets and timed musical annotations.** Transcribe a complete song, edit its ABC or MIDI, render a piano preview, or extract embeddings and token predictions for your own tools.
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View details ↗YuE-s1-0.5B
m-a-p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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