Saved toolsSubmit a tool
FOXAI SEARCH

Search FoxAI

AI Models 84759

  • WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGUF↗

    TheBloke

    This repo contains GGUF format model files for Monero's WizardLM-Uncensored-SuperCOT-Storytelling-30B. GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d

  • SN3807↗

    thieu86

  • Tifa-DeepsexV2-7b-MGRPO-GGUF-F16↗

    Tifa-RP

  • inception_v3.tv_in1k↗

    timm

    A Inception-v3 image classification model. Trained on ImageNet-1k, torchvision weights. - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 23.8 - GMACs: 5.7 - Activations (M): 9.0 - Image size: 299 x 299 - **Papers:** - Rethinking the Inception Architecture for Computer Vision: https://arxiv.org/abs/1512.00567 - **Original:** https://github.com/pytorch/vision - **Dataset:** ImageNet-1k Explore the dataset and runtime metrics of this model in timm model results.

  • Swift-1.5-Qwen3.8-27B-GGUF↗

    ukisai

    Website  •  Learn more  •  GGUF  •  GSQ-RCO GGUF  •  Evaluation  •  Enterprise licensing **GGUF quantizations.** Derived directly from Swift 1.5 with llama.cpp. Run a chosen quantization tier with a current llama.cpp-compatible runtime such as `llama-server`. Swift 1.5 Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B. It uses **58.5% fewer thinking tokens** while scoring **0.35% higher** than the base, for a **9.18× speed-up** on several tasks.

  • Ministral-3-3B-Base-2512-unsloth-bnb-4bit↗

    unsloth

  • Qwen3-VL-8B-Instruct↗

    unsloth

    This generation delivers comprehensive upgrades across the board: superior text understanding & generation, deeper visual perception & reasoning, extended context length, enhanced spatial and video dynamics comprehension, and stronger agent interaction capabilities. Available in Dense and MoE architectures that scale from edge to cloud, with Instruct and reasoning‑enhanced Thinking editions for flexible, on‑demand deployment. 1. **Interleaved-MRoPE**: Full‑frequency allocation over time, width, and height via robust positional embeddings, enhancing long‑horizon video reasoning.

  • Vikhr-Qwen-2.5-0.5B-instruct-GGUF↗

    Vikhrmodels

  • Vela-1.0-Encoder-307M-Halu↗

    vllm-sr

  • Sakura-Qwen3.8-Flash-Next-Swift-3bit-352E-GGUF↗

    webmp3