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AI 모델 85003

• gemma-4-26B-A4B-it-Claude-Opus-Distill-GGUF↗

  TeichAI

• NeuralTranslate_v0.2_GGUF↗

  Thermostatic

• controlnet-sd21-depth-diffusers↗

  thibaud

• vit_base_patch16_dinov3_qkvb.lvd1689m↗

  timm

  A DINOv3 ViT model image feature encoder. Distilled on LVD-1689M from the DINOv3 ViT-7B model. * The original model weights ended up with all QKV projection biases being zeroes. For `timm`, have disabled the QKV bias (`qkv_bias=False`) for the models and not loaded the zero weights. For some model sizes there are variants with `qkvb` in the name that have the bias enabled (`qkv_bias=True`), but zero, to match the behaviour of `transformers` and original models. * The original models keep RoPE periods as a persistent `bfloat16` buffer. `timm` generates `float32` periods at init. This results in some numerical differences, however the `timm` approach should be less problematic running on devices without bfloat16 support, and appears to work as well if not slightly better for fine-tuning. `model.rope.periods = model.rope.periods.to(torch.bfloat16).to(torch.float32)` will truncate the periods to bfloat16 and result in matching outputs. - **Model Type:** Image Feature Encoder - **Model Stats:** - Params (M): 85.7 - GMACs: 23.6 - Activations (M): 34.1 - Image size: 256 x 256 - **Original:** https://github.com/facebookresearch/dinov3 - **License:** DINOv3 - **Dataset:** LVD-1689M - **Papers:** - DINOv3: https://arxiv.org/abs/2508.10104 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models

• testing↗

  tmadge

  This modelcard aims to be a base template for new models. It has been generated using this raw template. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Z-Image-Turbo↗

  Tongyi-MAI

  ⚡️- Image An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer Z-Image is a powerful and highly efficient image generation model family with **6B** parameters. Currently there are four variants: - 🚀 **Z-Image-Turbo** – A distilled version of Z-Image that matches or exceeds leading competitors with only **8 NFEs** (Number of Function Evaluations). It offers **⚡️sub-second inference latency⚡️** on enterprise-grade H800 GPUs and fits comfortably within **16G VRAM consumer devices**. It excels in photorealistic image generation, bilingual text rendering (English & Chinese), and robust instruction adherence.

• tiny-NemotronHForCausalLM-nano↗

  trl-internal-testing

  This is a minimal model built for unit tests in the TRL library.

• japanese-gpt-neox-47M↗

  u-hyszk

• EDIATH-Q4_K_M↗

  user052

• Qwen2.5-Coder-32B-Instruct-GGUF↗

  Volko76