gemma-4-26B-A4B-it-UD-Q4_K_M-layers
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View details ↗DISCOVER & COMPARE
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qwen3.5-9b-uncensored-coder
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View details ↗qwen3-4b-html-spatial-gguf
Ashwin999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 ↗DiffuRefill-1B
Asilarkness**Status: pretraining, about half way.** Step ~100k of 200k, ~46B effective training tokens, merge round 290. The weights here are a live pretraining checkpoint: fluent English, facts still unreliable, not instruction-tuned. Use `global_ema.pt`; it is the best checkpoint at every round. A 1.08B masked-diffusion language model decoded with **confidence-targeted re-infilling**: draft the whole sequence in a handful of parallel denoising passes, then spend a few more passes rewriting only the tokens the model is least sure about. An autoregressive model needs one pass per token; this one needs ~20 passes for a whole sequence, whatever its length. Tracked every round on fixed, deterministic batches, so the change between two rounds carries no sampling noise:
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View details ↗GLM-5.3-Flash-Uncensored-NVFP4-Pruned-vLLM
Asilarkness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 ↗GLM-5.3-Flash-Uncensored-NVFP4-Pruning-62GB
Asilarkness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 ↗sub38-157
asim22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 ↗sub38-221
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View details ↗sub38-65
asim22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 ↗sub38-71
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View details ↗danAI-55M-Reasoning
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View details ↗Qwen3-Coder-30B-A3B-Instruct-GGUF
AskMeCodes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 ↗DiffRhythm2
ASLP-lab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 ↗SongFormer
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View details ↗YingMusic-Singer-Plus
ASLP-lab🎤 YingMusic-Singer-Plus: Controllable Singing Voice Synthesis with Flexible Lyric Manipulation and Annotation-free Melody Guidance Chunbo Hao 1,2 · Junjie Zheng 2 · Guobin Ma 1 · Yuepeng Jiang 1 · Huakang Chen 1 · Wenjie Tian 1 · Gongyu Chen 2 · Zihao Chen 2 · Lei Xie 1 1 Audio, Speech and Language Processing Group (ASLP@NPU), School of Computer Science, Northwestern Polytechnical University, China 2 AI Lab, GiantNetwork, China
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View details ↗mnistvit-transformers
asneltThe mnistvit vision transformer (ViT), trained on MNIST with 99.65% test set accuracy, packaged for the Hugging Face transformers library. The weights are a lossless conversion of the original checkpoint. The model itself is the standard transformers `ViTModel` with a small classification head, so no dependencies beyond transformers are needed. An ONNX export in `onnx/model.onnx` runs without Python or PyTorch, for example with ONNX Runtime in C++, C#, Java, mobile apps or the browser. The model and its image processor are defined in this repository, so loading requires `trust_remote_code=True`:
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View details ↗acge_text_embedding
aspire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 ↗MiniMax-H3-Turbo-Lora
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View details ↗Qwen3.6-27B-Fable-Fusion-711-Heretic2-GGUF
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View details ↗lebanese-llama-3.1-8b
assix-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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