Kimi-K2.5-P48-NVFP4-W4A4GS32
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LongCat-Video-dit-int8
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View details ↗styleid
kwanY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 ↗workvoice-qwen3.5-0.8b-ft
kwdowik00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 ↗Hemmingway-1-NInfer
kybrcore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-QUASAR-NVFP4-NInfer
kybrcore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 ↗primus_exploit_finetune
kygbjs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 ↗albert-kor-base
kykim* 70GB Korean text dataset and 42000 lower-cased subwords are used * Check the model performance and other language models for Korean in github
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View details ↗bert-kor-base
kykim* 70GB Korean text dataset and 42000 lower-cased subwords are used * Check the model performance and other language models for Korean in github
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View details ↗electra-kor-base
kykim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 ↗BrickGPT-Qwen3-1.7B-BrickMore-48
Kyle308666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_lora
kyledam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 ↗Qwopus-GLM-18B-Merged-GGUF
KyleHessling1A **64-layer frankenmerge** of two of Jackrong's incredible Qwen3.5-9B finetunes, stacking all 32 layers from each to create an ~18B parameter model, then **healed with a 1000-step QLoRA fine-tune** to smooth the layer boundary. **This was a fun experiment!** A lot of people have been asking for something between Jackrong's 27B and 9B models — something that runs well on 12–16 GB GPUs. This frankenmerge is an attempt at filling that gap, and the results are surprisingly good. The raw frankenmerge had a known issue: **garbled code output**. Because two separately-trained models were stacked at layer 32, structured output (code blocks, HTML, bracket matching) would occasionally come out malformed or hallucinated.
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View details ↗Qwopus3.6-27B-Fusion-GGUF
KyleHessling1> **Research preview.** This model attempts to combine the **reasoning** capability of > `Qwopus3.6-27B-v2` with the **code-execution** capability of `Qwopus3.6-27B-Coder` — in a single 27B > model, *without significant loss to either* skill. In practice it behaves like a production model > with both capabilities fused, but it has **not** yet been through a full rigorous evaluation. If you > find issues, please reach out on X — **@kylehessling1**. - **Format:** GGUF, `Q4_K_M` (~16.5 GB) — runs on a single 24–32 GB GPU via `llama.cpp`. - **Architecture:** `qwen35` hybrid (linear-attention + periodic full-attention), 27B params, 64 layers. - **Native context:** 262,144 tokens. Verified clean (needle + termination) to 60K. - **Modes:** thinking (reasoning) **on** by default; strong agentic/coding behavior. - **Vision:** 🖼️ **image input supported** via the companion projector `Qwopus3.6-27B-Fusion-mmproj-Q8_0.gguf` (~0.6 GB) — pair it with *any* quant below (see Vision). ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
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View details ↗needle-8b-gguf
KyrillosIshak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 ↗Ko-PlatYi-6B
kyujinpy**Model Architecture** Ko-PlatYi-6B is an auto-regressive language model based on the Yi-34B transformer architecture. **Training Dataset** kyujinpy/KOR-OpenOrca-Platypus-v3. > Follow up as link.
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View details ↗CASA-Qwen2_5-VL-3B
kyutai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 ↗helium-1-2b
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