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AI モデル 84753

• Qwen3-VL-8B-Instruct-Eagle3↗

  taobao-mnn

• Kimiko-v2-13B-GGUF↗

  TheBloke

• efficientnet_es.ra_in1k↗

  timm

• maxxvitv2_rmlp_base_rw_384.sw_in12k_ft_in1k↗

  timm

• regnety_064.ra3_in1k↗

  timm

  The `timm` RegNet implementation includes a number of enhancements not present in other implementations, including: * stochastic depth * gradient checkpointing * layer-wise LR decay * configurable output stride (dilation) * configurable activation and norm layers * option for a pre-activation bottleneck block used in RegNetV variant * only known RegNetZ model definitions with pretrained weights For the comparison summary below, the ra_in1k, ra3_in1k, ch_in1k, sw_*, and lion_* tagged weights are trained in `timm`.

• STAR1-R1-Distill-14B↗

  UCSC-VLAA

• MetaView-ComfyUI↗

  Wherebot101

• Xing4.0-29B-A4B-FP8↗

  XingChen-AGI

• ReMaRk-Qwen3.5-4B-RedditBooks-RankGRPO-s3407↗

  XINLI1997

• fashionpedia-finetuned↗

  yosikim-nearsolution