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Explore AI models 84991
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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 ↗danAI-55M-Reasoning
asjadilahi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-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
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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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View details ↗Huihui-Qwen3.8-27B-abliterated-GGUF
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View details ↗KEEP
Astaxanthin**KEEP** (**K**nowledg**E**-**E**nhanced **P**athology) is a foundation model designed for cancer diagnosis that integrates disease knowledge into vision-language pre-training. It utilizes a comprehensive disease knowledge graph (KG) containing 11,454 human diseases and 139,143 disease attributes, such as synonyms, definitions, and hierarchical relationships. KEEP reorganizes millions of publicly available noisy pathology image-text pairs into 143K well-structured semantic groups based on the hierarchical relations of the disease KG. By incorporating disease knowledge into the alignment process, KEEP achieves more nuanced image and text representations. The model is validated on 18 diverse benchmarks with over 14,000 whole-slide images (WSIs), demonstrating state-of-the-art performance in zero-shot cancer diagnosis, including an average sensitivity of 89.8% for cancer detection across 7 cancer types. KEEP also excels in subtyping rare cancers, achieving strong generalizability in diagnosing rare tumor subtypes. - **Developed by:** MAGIC-AI4Med team from Shanghai Jiao Tong University and Shanghai AI Lab. - **Model type:** Vision-language models (vision encoder: ViT-L/16; text encoder: Bert) - **Pretrain datasets:** 143K pathology semantic groups, each with a single caption and multiple images. - **License:** MIT - **Repository:** https://github.com/MAGIC-AI4Med/KEEP - **Paper [optional]:** https://www.cell.com/cancer-cell/fulltext/S1535-6108(26)00058-9 - **Demo [optional]:** [More Information Needed]
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View details ↗hablalo-small100-coreml
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