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AI Models 84759

• Falcon-H1-7B-Base↗

  tiiuae

• efficientnetv2_rw_m.agc_in1k↗

  timm

  A EfficientNet-v2 image classification model. This is a `timm` specific variation of the architecture. Trained on ImageNet-1k in `timm` using recipe template described below. Recipe details: * Based on ResNet Strikes Back `C` recipes * SGD (w/ Nesterov) optimizer and AGC (adaptive gradient clipping). * Cosine LR schedule with warmup - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 53.2 - GMACs: 12.7 - Activations (M): 47.1 - Image size: train = 320 x 320, test = 416 x 416 - **Papers:** - EfficientNetV2: Smaller Models and Faster Training: https://arxiv.org/abs/2104.00298 - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - **Dataset:** ImageNet-1k - **Original:** https://github.com/huggingface/pytorch-image-models

• resnet101.a1_in1k↗

  timm

  This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Recipe details: * ResNet Strikes Back `A1` recipe * LAMB optimizer with BCE loss * Cosine LR schedule with warmup - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 44.5 - GMACs: 7.8 - Activations (M): 16.2 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/huggingface/pytorch-image-models

• tf_efficientnet_l2.ns_jft_in1k_475↗

  timm

• vit_base_patch16_224_miil.in21k↗

  timm

• SmolLM2-1.7B-Instruct-GGUF↗

  unsloth

• gemma-3-12b-it-qat-GGUF↗

  Uynaity

• larger_clap_music_and_speech↗

  Xenova

  https://huggingface.co/laion/larger_clap_music_and_speech with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

• Helcyon-Aurora-12b-v1.1-GGUF↗

  XeyonAI

• BiRefNet_lite-matting↗

  ZhengPeng7