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AI 모델 84759

• Qwen3.6-27B-Uncensored-HauhauCS-Aggressive↗

  HauhauCS

  > **Join the Discord** for updates, roadmaps, projects, or just to chat. > **Not sure which variant to pick?** 99.9%+ of users should use **Balanced** — same 0/465 refusal rate, more stable sampling, great for agentic coding / tool-use / reasoning / creative writing. Pick **Aggressive** only if you specifically want the model to skip its preamble on hardcore prompts. > **HuggingFace's "Hardware Compatibility" widget doesn't recognize K_P quants** — it may show fewer files than actually exist. Click **"View +X variants"** or go to **Files and versions** to see all available downloads.

• opus-mt-tc-big-en-ro↗

  Helsinki-NLP

• opus-mt-tc-big-it-en↗

  Helsinki-NLP

• chinese-llama-2-7b-gguf↗

  hfl

• ViT-B-16-SigLIP-i18n-256__webli↗

  immich-app

• gemma-4-12B-it-int4-AutoRound↗

  Intel

• internlm2-7b-reward↗

  internlm

• fb229049↗

  jkjjbk

• Wan2.2SprayLora↗

  JorisHolshuijsen

• ms-eff-gcvit-deepfake-b5-kodf↗

  KoreaPeter

  Multi-Scale Efficient Global Context Vision Transformer (MS-EffGCViT) is a hybrid CNN-ViT architecture for **deepfake detection**. It fuses CNN-driven spatial inductive bias with hierarchical global-context attention to catch both *local* artifacts (textures, blending seams) and *global* artifacts (lighting, structural inconsistency). A single architecture ships in two sizes and three domain-tuned checkpoints, working on both **static images** and **video** at the **frame level**. While traditional Vision Transformers (ViTs) utilize a Linear Projection for patch embedding, our proposed model adopts a CNN-based Patch Embedding module incorporating MBConvBlocks.