Qwen3.5-9B-Python-Coder-GGUF
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探索 AI 模型 85001
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laion> 对比学习在多模态表示学习领域取得了显著成效。在本文中，我们提出了一种对比语言-音频预训练流程，通过将音频数据与自然语言描述相结合来构建音频表示。为实现这一目标，我们首先发布了 LAION-Audio-630K，这是一个包含来自不同数据源的 633,526 对音频-文本样本的大规模数据集。其次，我们设计并构建了一个对比语言-音频预训练模型，综合考虑了不同的音频编码器和文本编码器。我们在模型设计中融入了特征融合机制和关键词到字幕的增强方法，以进一步提升模型处理变长音频输入的能力并优化性能。第三，我们开展了全面的实验，从三个任务对我们的模型进行评估：文本到音频检索、零样本音频分类以及监督式音频分类。结果表明，我们的模型在文本到音频检索任务中表现出色。在音频分类任务中，该模型在零样本设置下达到了最先进的性能，并且在非零样本设置下的表现也能够与现有模型相媲美。LAION-Audio-630K 数据集及所提出的模型均已向公众开放。您可以将该模型用于零样本音频分类，或用于提取音频和/或文本特征。
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laion0. TL;DR 1. Model Details 2. Usage 3. Uses 4. Citation > Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public. You can use this model for zero shot audio classification or extracting audio and/or textual features.
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查看详情 ↗A series of CLIP ConvNeXt-Base (w/ wide embed dim) models trained on subsets LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Goals: * Explore an alternative to ViT and ResNet (w/ AttentionPooling) CLIP models that scales well with model size and image resolution Firsts: * First known ConvNeXt CLIP models trained at scale in the range of CLIP ViT-B/16 and RN50x4 models * First released model weights exploring increase of augmentation + regularization for image tower via adding (greater scale range of RRC, random erasing, stochastic depth)
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查看详情 ↗1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation A series of CLIP ConvNeXt-Base (w/ wide embed dim) models trained on subsets LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Goals: * Explore an alternative to ViT and ResNet (w/ AttentionPooling) CLIP models that scales well with model size and image resolution
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查看详情 ↗1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation A series of CLIP ConvNeXt-Large (w/ extra text depth, vision MLP head) models trained on the LAION-2B (english) subset of LAION-5B using OpenCLIP. The models utilize: * the timm ConvNeXt-Large model (`convnext_large`) as the image tower * a MLP (`fc - gelu - drop - fc`) head in vision tower instead of the single projection of other CLIP models * a text tower with same width but 4 layers more depth than ViT-L / RN50x16 models (depth 16, embed dim 768).
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查看详情 ↗1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation A series of CLIP ConvNeXt-XXLarge (a custom `timm` ConvNeXt size) models trained on LAION-2B (english), a subset of LAION-5B, using OpenCLIP. The core training run was performed in pieces over a period of ~ 2 months. The global batch size for the core run was 81920. The last ~10% of training was re-done at a 95744 global batch size w/ higher LR and aug than original finish. The two were averaged together in a 'soup'. See more details in Training Details.
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查看详情 ↗1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation 7. How To Get Started With the Model A CLIP ViT-B/16 model trained with the DataComp-1B (https://github.com/mlfoundations/datacomp) using OpenCLIP (https://github.com/mlfoundations/open_clip). As per the original OpenAI CLIP model card, this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.
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查看详情 ↗1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation A CLIP ViT-B/16 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). Model training done by Mehdi Cherti on the JUWELS Booster supercomputer. See acknowledgements below.
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查看详情 ↗1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. How To Get Started With the Model 6. Acknowledgements 7. Citation A CLIP ViT-B/32 model trained with the DataComp-1B (https://github.com/mlfoundations/datacomp) using OpenCLIP (https://github.com/mlfoundations/open_clip) at 256x256 resolution. As per the original OpenAI CLIP model card, this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.
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查看详情 ↗1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation 7. How To Get Started With the Model A CLIP ViT-B/32 model trained with the DataComp-1B (https://github.com/mlfoundations/datacomp) using OpenCLIP (https://github.com/mlfoundations/open_clip). As per the original OpenAI CLIP model card, this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.
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查看详情 ↗1. Model Details 2. Uses 3. Training Details 4. Evaluation 5. Acknowledgements 6. Citation 7. How To Get Started With the Model A CLIP ViT-B/32 model trained with the LAION-2B English subset of LAION-5B (https://laion.ai/blog/laion-5b/) using OpenCLIP (https://github.com/mlfoundations/open_clip). As per the original OpenAI CLIP model card, this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。