Qwen3-1.7B-Pingala8-2bit
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Qwen3-4B-Pingala8-2bit
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View details ↗Qwen3.5-0.8B-Pingala8-2bit
Genrius-AI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 ↗Qwen3.5-2B-Pingala8-2bit
Genrius-AI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 ↗Qwen3.5-4B-Pingala8-2bit
Genrius-AI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 ↗baberu-ocr
genshiai-daichi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 ↗med-ruri-v3-310m-v2-from-med
genshiai-daichi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 ↗open-1b-base
Gensyn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 ↗OS-Test-Mark-GEN-I
GenTrendGPT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 ↗Wan_2.2_ComfyUI_Repackaged
GenXDad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 ↗sfm_unfiltered_e2e_alignment_upsampled_base
geodesic-research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 ↗sfm_unfiltered_e2e_misalignment_upsampled_base
geodesic-research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 ↗iMatrix
geoffmunn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 ↗PeerReviewers
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View details ↗Qwen3-0.6B-f16
geoffmunnThis is a **GGUF-quantized version** of the **Qwen/Qwen3-0.6B** language model — a compact **600-million-parameter** LLM designed for **ultra-fast inference on low-resource devices**. Converted for use with `llama.cpp`, LM Studio, OpenWebUI, and GPT4All, enabling private AI anywhere — even offline. > ⚠️ **Note**: This is a *very small* model. It will not match larger models (e.g., 4B+) in reasoning, coding, or factual accuracy. However, it shines in **speed, portability, and efficiency**.
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View details ↗Qwen3-1.7B-f16
geoffmunnThis is a **GGUF-quantized version** of the **Qwen/Qwen3-1.7B** language model — a balanced **1.7-billion-parameter** LLM designed for **efficient local inference with strong reasoning and multilingual capabilities**. The **Qwen3-1.7B** model offers a compelling middle ground between ultra-lightweight and full-scale language models, delivering: - **Noticeably better coherence and reasoning** than 0.5B–1B models - **Fast CPU inference** with minimal latency—ideal for real-time applications - **Quantized variants that fit in ~3–4 GB RAM**, making it suitable for low-end laptops, tablets, or edge devices - **Strong multilingual and coding support** inherited from the Qwen3 family It’s ideal for: - Responsive on-device assistants with more natural conversation flow - Lightweight agent systems that require step-by-step logic - Educational projects or hobbyist experiments with meaningful capability - Prototyping AI features before scaling to larger models
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View details ↗Qwen3-14B-f16
geoffmunn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 ↗Qwen3-30B-A3B-Thinking-2507
geoffmunn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 ↗Qwen3-32B-f16
geoffmunn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 ↗Qwen3-4B-f16
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View details ↗Qwen3.5-122B-A10B-f16
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View details ↗Qwen3.5-397B-A17B-f16
geoffmunn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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