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AI 模型 84759

• NexoNimbus-7B-GGUF↗

  TheBloke

• vicuna-33B-coder-GGUF↗

  TheBloke

• regnetx_002.pycls_in1k↗

  timm

  A RegNetX-200MF image classification model. Pretrained on ImageNet-1k by paper authors. 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 - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 2.7 - GMACs: 0.2 - Activations (M): 2.2 - Image size: 224 x 224 - **Papers:** - Designing Network Design Spaces: https://arxiv.org/abs/2003.13678 - **Dataset:** ImageNet-1k - **Original:** https://github.com/facebookresearch/pycls

• tresnet_m.miil_in21k↗

  timm

• Qwen3.6-27B-M↗

  trymirai

• LTX-2-FP8↗

  unsloth

• flux-female-anatomy↗

  uriel353

• t5-base-e2e-qg↗

  valhalla

  This is t5-base model trained for end-to-end question generation task. Simply input the text and the model will generate multile questions.

• d3-edge↗

  vllm-sr

  **d3-edge** is the 0.6B 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 Semantic Router serves Decision 3.0 natively in Engine mode, which runs one model behind the System One API on your own machine. Its answers matched this package's own `d3_server.py` exactly, for text, images and videos, with one request per forward, the former default (measured on AMD Instinct MI325X); with batching, now the default, a few near-tie decisions change (Serving performance).

• c67-h11↗

  wandb08