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AI Models 84759

• Sequential-Hidden-Decoding-8B-n8↗

  腾讯

• clip-vit-base-patch32_oxford_flowers102↗

  tanganke

• d38a11↗

  tao1000

• vit_base_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): 86.6 - GMACs: 16.9 - Activations (M): 16.5 - 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.

• xception41p.ra3_in1k↗

  timm

• d3↗

  vllm-sr

  **d3** is the 27B multimodal foundation decision model of Decision 3.0, the decision models of vLLM Semantic Router. Give it an input (text or JSON, optionally with images and videos) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all in one call and returns a probability for every answer, without generating text. Images go before the text of the request, each read at up to 1.6 megapixels; videos follow the images, read at 2 frames per second (at most 32 frames spread over the whole video, each at up to 0.2 megapixels). Every question of the request sees all of them. vLLM serves d3 natively: `vllm serve` loads this repository as it is, with no conversion, and answers on vLLM's System One endpoint, `/v1/systemone`. The support is on vLLM's main branch since 2026-10-14 (vllm-project/vllm#61565, commit `89cab55`), and no vLLM release includes it yet (the latest is v0.31.0). Until one does, install a current nightly build with uv; vLLM's nightly indexes do not work with plain `pip`:

• whisper-large-v3-turbo-gguf↗

  voconly-org

• DeepSeek-V4.1-Flash-Engram-IQ4_NL-NInfer-v3↗

  WaveCut

• Sakura-Fara1.5-9B-GGUF↗

  webmp3

  Fara1.5-9B is Microsoft's computer-use agent for web browsing (Qwen3.5-9B base, multimodal: it reads browser screenshots and emits structured tool calls). This repository holds **three GGUF files of different size** made from the BF16 weights with our measured mixed-codec method. Pick one on the *Files* page; the table below says what each file is. Independent community quantization, **not** an official Microsoft release. Part of the **Sakura Mini** line. Same method, same texts, same machine for every file; lower KL divergence means closer to the BF16 original: Why: the bit budget per tensor is allocated from measured sensitivity (see *How it was made*), not from fixed rules. Full table below.

• Iona-v01↗

  xandorx