Qwen3.5-9B-TAP-DPQ-v6-GGUF
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Qwen3.5-9B-TAP-DPQ-v7-GGUF
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View details ↗Qwen3.5-9B-TAP-DPQ-v8-GGUF
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View details ↗Qwen3.8-27B-TAP-DPQ-1.75bpw-GGUF
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View details ↗Qwen3.8-27B-TAP-DPQ-2.72bpw-GGUF
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View details ↗Qwen3.8-27B-TAP-DPQ-3.50bpw-GGUF
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View details ↗Qwen3.8-27B-TAP-DPQ-v2-GGUF
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View details ↗Qwen3.8-27B-TAP-DPQ-v4-2.06bpw-GGUF
DuoNeural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 ↗Qwen3.8-27B-TAP-DPQ-v5-2.14bpw-GGUF
DuoNeural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 ↗Qwen3-8B-Abliterated-GGUF
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View details ↗NetaYume-Lumina-Image-2.0
duongveNetaYume Lumina is a text-to-image model fine-tuned from Neta Lumina, a high-quality anime-style image generation model developed by Neta.art Lab. It builds upon Lumina-Image-2.0, an open-source base model released by the Alpha-VLLM team at Shanghai AI Laboratory. This model was trained with the goal of not only generating realistic human images but also producing high-quality anime-style images. Despite being fine-tuned on a specific dataset, it retains a significant amount of knowledge from the base model. **Key Features:** - **High-Quality Anime Generation**: Generates detailed anime-style images with sharp outlines, vibrant colors, and smooth shading. - **Improved Character Understanding**: Better captures characters, especially those from the Danbooru dataset, resulting in more coherent and accurate character representations. - **Enhanced Fine Details**: Accurately generates accessories, clothing textures, hairstyles, and background elements with greater clarity.
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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 ↗Bonsai-27B-dSpark-MLX-4bit
dusterbloom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 ↗XuanYuan-6B
Duxiaoman-DI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 ↗XuanYuan-70B
Duxiaoman-DI考虑到金融场景下存在非常多长文本的业务，基于我们高效的分布式训练框架，我们将模型的上下文长度在预训练阶段从4k扩充到了8k和16k，据我们所知，这也是首个在70B参数量级上达到8k及以上上下文长度的开源大模型。 具体细节参考：XuanYuan-70B 我们自研的指令微调数据预期模型能够在通用对话能力保留的同时，更加侧重金融领域的问答。具体来说，通用指令数据分为以下几个大类：常识百科、代码编程、逻辑推理、数学计算、创意生成、安全无害、摘要提取、翻译等。其中每一大类下又设计了多个子类，来尽可能保证指令数据的多样性和丰富度。 在训练过程中，我们同样保持8k的上下文长度，未采取外推的措施来提升上下文。后续我们将继续在预训练阶段来提升上下文长度。 同时训练数据中的question-answer pair，我们仅对answer部分计算损失。
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View details ↗XuanYuan-70B-Chat
Duxiaoman-DI考虑到金融场景下存在非常多长文本的业务，基于我们高效的分布式训练框架，我们将模型的上下文长度在预训练阶段从4k扩充到了8k和16k，据我们所知，这也是首个在70B参数量级上达到8k及以上上下文长度的开源大模型。 具体细节参考：XuanYuan-70B 我们自研的指令微调数据预期模型能够在通用对话能力保留的同时，更加侧重金融领域的问答。具体来说，通用指令数据分为以下几个大类：常识百科、代码编程、逻辑推理、数学计算、创意生成、安全无害、摘要提取、翻译等。其中每一大类下又设计了多个子类，来尽可能保证指令数据的多样性和丰富度。 在训练过程中，我们同样保持8k的上下文长度，未采取外推的措施来提升上下文。后续我们将继续在预训练阶段来提升上下文长度。 同时训练数据中的question-answer pair，我们仅对answer部分计算损失。
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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 ↗Aethora-7b-v1-imatrix-GGUF
duyntnet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 ↗AlphaMonarch-7B-imatrix-GGUF
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View details ↗aya-expanse-8b-imatrix-GGUF
duyntnet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 ↗bagel-8b-v1.0-imatrix-GGUF
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View details ↗Blue-Orchid-2x7b-imatrix-GGUF
duyntnet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 ↗Blurdus-7b-v0.1-imatrix-GGUF
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