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AI 모델 84759

• maxvit_large_tf_384.in1k↗

  timm

• vit_small_patch16_224.augreg_in21k_ft_in1k↗

  timm

  A Vision Transformer (ViT) image classification model. Trained on ImageNet-21k and fine-tuned on ImageNet-1k (with additional augmentation and regularization) in JAX by paper authors, ported to PyTorch by Ross Wightman. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 22.1 - GMACs: 4.3 - Activations (M): 8.2 - Image size: 224 x 224 - **Papers:** - How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers: https://arxiv.org/abs/2106.10270 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - **Dataset:** ImageNet-1k - **Pretrain Dataset:** ImageNet-21k - **Original:** https://github.com/google-research/vision_transformer Explore the dataset and runtime metrics of this model in timm model results.

• toxigen_roberta↗

  tomh

  This model comes from the paper ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection and can be used to detect implicit hate speech.

• gemma-4-E4B-it-uncensored↗

  TrevorJS

• SapBERT-from-PubMedBERT-fulltext_bf16↗

  UMCU

• Qwen3VL-8B-Uncensored-HauhauCS-Aggressive↗

  Unrestricted

• LFM2-2.6B-Exp-GGUF↗

  unsloth

• Qwen3.5-9B-GGUF-MoQ↗

  w-ahmad

  >MoQ (Mixture of Quants) is a smart way to shrink AI models without losing their "brainpower." Unlike old methods that treat every part of the model the same, MoQ identifies the most important parts and keeps them high-quality, while heavily compressing the rest to save space.****Stop settling for uniform bitrates.** Standard quantization is a relic of the past, treating vital cognitive weights the same as redundant noise. ** --- The result? A model that punches significantly above its weight class. Benjamin Marie evaluated MoQ GGUFs ("Mixture of Quants") against Unsloth Dynamic (UD) quants, focusing on low-bit versions below 4 bits on average — the range where GGUF models typically struggle most. Results: At similar bits-per-weight (Bpw), MoQ outperforms Unsloth Dynamic quants by ~10% on benchmarks, while also being roughly 2× more token-efficient on average. --- | Folder Link | BPW | Total Size | Description | | :--- | :---: | :---: | :--- | | 📂 **BF16** | **16** | **17.92 GB** | 📂 **F16** | **16** | **17.92 GB** | 📂 **MoQ-Quants-Latest** | **3.2** | **3.58 GB** | 📂 **MoQ-Quants-Latest** | **3.6** | **4.03 GB** | 📂 **MoQ-Quants-Latest** | **3.8** | **4.22 GB** | 📂 **MoQ-Quants-Latest** | **4.1** | **4.64 GB** | 📂 **MoQ-Quants-Latest** | **4.3** | **4.84 GB** | 📂 **MoQ-Quants-Latest** | **4.6** | **5.11 GB** | 📂 **MoQ-Quants-Latest** | **4.8** | **5.37 GB** | 📂 **MoQ-Quants-Latest** | **4.9** | **5.49 GB** | 📂 **MoQ-Quants-Latest** | **5.1** | **5.75 GB** | 📂 **MoQ-Quants-Latest** | **5.3** | **5.92 GB** | 📂 **MoQ-Quants-Latest** | **6.6** | **7.36 GB** | 📂 **MoQ-mmproj** | **16.0** | **0.92 GB**

• MiniMax-H3↗

  x-atm-x

• distilbert-base-uncased↗

  Xenova