Saved toolsSubmit a tool
FOXAI SEARCH

Search FoxAI

AI Models 85001

  • Minueza-32M-Base↗

    Felladrin

  • Qwen2.5-Coder-1.5B-Instruct-GGUF↗

    gaianet

  • pix2struct-textcaps-large↗

    Google

  • opus-mt-de-en↗

    Helsinki-NLP

    * source languages: de * target languages: en * OPUS readme: de-en * dataset: opus * model: transformer-align * pre-processing: normalization + SentencePiece * download original weights: opus-2020-02-26.zip * test set translations: opus-2020-02-26.test.txt * test set scores: opus-2020-02-26.eval.txt

  • opus-mt-tc-big-el-en↗

    Helsinki-NLP

  • GingerMix↗

    Hemlok

  • granite-3.2-8b-instruct-GGUF↗

    ibm-research

  • Qwen3.8-27B-cmf↗

    infosave

    Qwen3.8-27B (48 GatedDeltaNet linear-attention layers + 16 full-attention layers, 262k context, thinking mode) packaged as a single CMF file. It runs with `cortiq`, a Rust inference engine with no Python and no ML framework: NVIDIA, AMD and Intel GPUs through Vulkan/DX12, Apple silicon through Metal, and the CPU. Use cortiq 0.8.15 or later for fast prompt processing; prebuilt binaries for Linux, macOS and Windows are on the releases page. The engine picks the GPU and its settings itself. `--no-think` asks for a direct answer; without it the model reasons first, so raise `--max-tokens` (default 256). The 4-bit files are quantized directly from the bf16 checkpoint. Perplexity: twelve 512-token windows of wikitext-2.

  • mmBERT-small↗

    jhu-clsp

    > TL;DR: A state-of-the-art multilingual encoder trained on 3T+ tokens across 1800+ languages, introducing novel techniques for learning low-resource languages during the decay phase. mmBERT is a modern multilingual encoder that significantly outperforms previous generation models like XLM-R on classification, embedding, and retrieval tasks. Built on the ModernBERT architecture with novel multilingual training innovations, mmBERT demonstrates that low-resource languages can be effectively learned during the decay phase of training. It is also significantly faster than any previous multilingual encoder. - Highlights - Quick Start - Model Description - Novel Training Innovations - Model Family - Training Data - Usage Examples - Fine-tuning Examples - Model Architecture - Citation

  • GLM-4-32B-0414-DASHQ-Q2-GGUF↗

    jkim96