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artificialguybr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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mochi-GGUF
artindnr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 ↗Strawberry-i1-GGUF
artindnr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 ↗tea-GGUF
artindnr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 ↗modernbert-base-tr-uncased
artiwise-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 ↗gemma4-comfyui-mcp
artokun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 ↗**ROCmFPX quantized GGUF (7 tiers + vision mmproj)** of huihui-ai/Huihui-Ornith-1.5-35B-A3B-abliterated — abliterated BF16 weights, locally quantized. > **Base model license**: `ornith-ai/Ornith-1.5-35B-A3B` is MIT-licensed; its copyright notice and license text are retained. The packaging of this quantization is Apache-2.0. This repository uses strix.cpp, a llama.cpp fork **specifically tuned for the AMD Strix Halo `gfx1151` (Ryzen AI MAX+ 395) UMA architecture and ROCm/HIP backend**, with native support for the ROCmFPX quantization format.
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View details ↗Ornith-1.5-35B-A3B-ROCmFPX
ArtomYuan**ROCmFPX quantized GGUF (7 tiers + vision mmproj)** of ornith-ai/Ornith-1.5-35B-A3B — the official base model, locally requantized. > **Base model license**: `ornith-ai/Ornith-1.5-35B-A3B` is MIT-licensed; its copyright notice and license text are retained. The packaging of this quantization is Apache-2.0. This repository uses strix.cpp, a llama.cpp fork **specifically tuned for the AMD Strix Halo `gfx1151` (Ryzen AI MAX+ 395) UMA architecture and ROCm/HIP backend**, with native support for the ROCmFPX quantization format.
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View details ↗Qwen3.8-27B-abliterated-ROCmFPX
ArtomYuan**ROCmFPX quantized GGUF (7 tiers + vision mmproj)** of huihui-ai/Huihui-Qwen3.8-27B-abliterated — abliterated BF16 weights, locally requantized. This repository uses strix.cpp, a llama.cpp fork **specifically tuned for the AMD Strix Halo `gfx1151` (Ryzen AI MAX+ 395) UMA architecture and ROCm/HIP backend**, with native support for the ROCmFPX quantization format. Requires strix.cpp (this fork supports ROCmFPX; **upstream llama.cpp cannot load these files** — format note at the end).
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View details ↗**ROCmFPX quantized GGUF (7 tiers + vision mmproj)** of huihui-ai/Huihui-Qwen3.8-27B-Coder390-EfficientThink-abliterated — abliterated BF16 weights of the Coder390-EfficientThink finetune, locally requantized. This repository uses strix.cpp, a llama.cpp fork **specifically tuned for the AMD Strix Halo `gfx1151` (Ryzen AI MAX+ 395) UMA architecture and ROCm/HIP backend**, with native support for the ROCmFPX quantization format. Requires strix.cpp (this fork supports ROCmFPX; **upstream llama.cpp cannot load these files** — format note at the end).
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View details ↗Qwen3.8-27B-ROCmFPX
ArtomYuan**ROCmFPX quantized GGUF (7 tiers + vision mmproj)** of Qwen/Qwen3.8-27B — official BF16 weights, locally requantized. This repository uses strix.cpp, a llama.cpp fork **specifically tuned for the AMD Strix Halo `gfx1151` (Ryzen AI MAX+ 395) UMA architecture and ROCm/HIP backend**, with native support for the ROCmFPX quantization format. Requires strix.cpp (this fork supports ROCmFPX; **upstream llama.cpp cannot load these files** — format note at the end).
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View details ↗Qwen3.8-Flash-Next-ROCmFPX
ArtomYuan**ROCmFPX quantized GGUF of Qwen3.8-Flash-Next (7 tiers + vision mmproj)** — locally requantized from the official Qwen/Qwen3.8-Flash-Next. > **License**: this quantization follows the base model's Qwen Community License 1.0. It is **not** Apache-2.0. Requires strix.cpp (this fork supports ROCmFPX; **upstream llama.cpp cannot load this batch** — see the Format Note at the end).
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View details ↗SraVaani-0.5-live
ARTPARK-IISc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 ↗SraVaani-1.0
ARTPARK-IISc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 ↗Vaani-LID_v0
ARTPARK-IISc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 ↗whisper-large-v3-vaani-hindi
ARTPARK-IIScThis is a fine-tuned version of OpenAI's Whisper-Large-V3, trained on approximately 718 hours of transcribed Hindi speech from multiple datasets. The models has finetuned using folllowing dataset Vaani ,Gramvaani IndicVoices, Fleurs,IndicTTS and Commonvoice The performance of the model was evaluated using multiple datasets, and the evaluation results are provided below.
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View details ↗L-MChat-7b
ArtplesL-MChat-7b is a merge of the following models: * Nexusflow/Starling-LM-7B-beta * FuseAI/FuseChat-7B-VaRM
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View details ↗L-MChat-Small
ArtplesThis was a test of mine how small merges perform, because there are a lot of 7b merges and higher but not a lot of 2b merges. The following models were included in the merge: * rhysjones/phi-2-orange-v2 * Weyaxi/Einstein-v4-phi2 Use it with the ChatML format, you can also use the Inference-API for this Model. Detailed results can be found here
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View details ↗yvvki_Erotophobia-24B-v1.1-GGUF
ArtusDev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 ↗pythia-14m-dd-rus-draft
ArtyomSubDiv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 ↗yolo11l-food-segmentation
arunapb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 ↗arunav_flux
arunav007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 ↗Bangla-twoclass-Sentiment-Analyzer
ArunavaonlyThis model is a fine-tuned version of xlm-roberta-base on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.7755 - F1: 0.6113 The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1800 - mixed_precision_training: Native AMP - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
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