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Holo-3.1-35B-A3B-GGUF
barozp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 ↗r17-v1
barreiros15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-AWQ-4bit
barrydeenAWQ **W4A16** quantization of `Qwen/Qwen3.8-27B`, produced with llm-compressor in `compressed-tensors` `pack-quantized` format — the best-optimised Marlin path on Ampere (SM 8.6). Qwen3.8-27B is a **hybrid** model. Only **16 of its 64** decoder layers use full attention; the other **48 use Gated DeltaNet** (`linear_attn.*`). Those DeltaNet projections are extremely sensitive to quantization, so they are left at full BF16 precision here, along with the vision tower and the multi-token-prediction head. That means only **256 Linear modules** are quantized — 16 layers × 4 attention projections plus 64 layers × 3 MLP projections — rather than the ~448 you would get by assuming a uniform architecture. Keeping 48 layers of DeltaNet in BF16 costs roughly 7 GB versus builds that quantize everything, and that is the entire difference in file size.
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View details ↗nb-whisper-coreml
Barrymanalow**This is a format conversion, not a new model.** No retraining, no fine-tuning, no distillation. Weights and behaviour are NbAiLab's; the changes are the PyTorch → CoreML conversion and the folder layout WhisperKit expects. All credit for the model belongs to NbAiLab. Float16 activations. The layout mirrors `argmaxinc/whisperkit-coreml`, which is what `WhisperKitConfig(model:modelRepo:)` expects to find. With papegøye, a bilingual Norwegian/English macOS dictation daemon — it downloads this repo automatically:
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View details ↗14B-Qwen2.5-Freya-x1-GGUF
bartowski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 ↗2B_or_not_2B-GGUF
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View details ↗32B-Qwen2.5-Kunou-v1-GGUF
bartowski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 ↗35b-beta-long-GGUF
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View details ↗70B-L3.3-Cirrus-x1-GGUF
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View details ↗72B-Qwen2.5-Kunou-v1-GGUF
bartowski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 ↗a-m-team_AM-Thinking-v1-GGUF
bartowski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 ↗Accio-Lab_occamy-1.0-GGUF
bartowski**Model details:** - Parameter count: 35B - Input support: text, image (with mmproj file) - details - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (22.32GB) - usually a good mix of size and performance. Download instructions available here The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:
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View details ↗AesCode-32B-GGUF
bartowski**Model details:** - Parameter count: 33B (source checkpoint) - Input support: text, image (with mmproj file) - details - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (20.49GB) - usually a good mix of size and performance. Download instructions available here The files marked `true` in the Split column above are stored as multiple parts in a folder. To download all the parts to a local folder, run:
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View details ↗AesCode-8B-GGUF
bartowski**Model details:** - Parameter count: 9B (source checkpoint) - Input support: text, image (with mmproj file) - details - Speculative decoding: no - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (5.21GB) - usually a good mix of size and performance. Download instructions available here These quants were made with llama.cpp release b11490 - if this model's architecture is newly supported, you'll need that release or newer to run them.
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View details ↗AgentFlow_agentflow-planner-7b-GGUF
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