Qwen3-14B-GGUF
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Qwen3-30B-A3B-GGUF
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View details ↗Qwen3-32B-GGUF
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View details ↗Qwen3-4B-GGUF
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View details ↗Qwen3-4B-Instruct-2507-Q8_0-GGUF
ggml-orgThis model was converted to GGUF format from `Qwen/Qwen3-4B-Instruct-2507` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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View details ↗Qwen3.5-0.8B-Base-GGUF
ggml-org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 ↗Qwen3.5-0.8B-GGUF
ggml-org> [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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View details ↗Qwen3.5-35B-A3B-GGUF
ggml-org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 ↗Qwen3.6-27B-GGUF
ggml-org- https://huggingface.co/Qwen/Qwen3.6-27B - https://huggingface.co/z-lab/Qwen3.6-27B-DFlash > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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View details ↗Qwen3.6-27B-MTP-GGUF
ggml-org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 ↗Qwen3.6-35B-A3B-GGUF
ggml-org- https://huggingface.co/Qwen/Qwen3.6-35B-A3B - https://huggingface.co/z-lab/Qwen3.6-35B-A3B-DFlash > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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View details ↗Qwen3.6-35B-A3B-MTP-GGUF
ggml-org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-GGUF
ggml-org- https://huggingface.co/Qwen/Qwen3.8-27B - https://huggingface.co/incoai/Qwen3.8-27B-DFlash2 > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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View details ↗Qwen3.8-Flash-Next-GGUF
ggml-org- https://huggingface.co/Qwen/Qwen3.8-Flash-Next - https://huggingface.co/incoai/Qwen3.8-Flash-Next-DFlash2 - `Q8_0` and `IQ4_NL`: all routed experts use the base quantization type. - `Q2_K`: routed gate/up experts at Q2_K, routed down experts at IQ4_NL. - Small non-expert tensors (embeddings, PLE embeddings, attention, shared experts) are kept at Q8_0, the PLE embedding is at Q4_0. - Includes a Q8_0 mmproj for the vision encoder, MTP sidecars (Q8_0 / Q4_0) and a DFlash2 draft sidecar (BF16 / Q8_0 / Q4_0) for speculative decoding. - The `Q2_K` routed gate/up expert tensors are calibrated with the imatrix from https://huggingface.co/AesSedai/Qwen3.8-Flash-Next-GGUF > [!IMPORTANT] > This model is automatically converted using https://github.com/ggml-org/convert
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View details ↗Qwen3-8B-Base-GGUF
ggml-org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-GGUF
ggml-org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-ASR-0.6B-GGUF
ggml-orgThis model is converted from Qwen/Qwen3-ASR-0.6B to GGUF using `convert_hf_to_gguf.py`
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View details ↗Qwen3-ASR-1.7B-GGUF
ggml-orgThis model is converted from Qwen/Qwen3-ASR-1.7B to GGUF using `convert_hf_to_gguf.py`
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View details ↗This model was converted to GGUF format from `Qwen/Qwen3-Coder-30B-A3B-Instruct` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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View details ↗Qwen3-Coder-Next-GGUF
ggml-org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-Omni-30B-A3B-Instruct-GGUF
ggml-orgThis model is converted from Qwen/Qwen3-Omni-30B-A3B-Instruct to GGUF using `convert_hf_to_gguf.py`
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View details ↗Qwen3-Omni-30B-A3B-Thinking-GGUF
ggml-orgThis model is converted from Qwen/Qwen3-Omni-30B-A3B-Thinking to GGUF using `convert_hf_to_gguf.py`
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