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AI Models 85001

• TheDrummer_Big-Alice-28B-v1-GGUF↗

  bartowski

• figlet-font-sgd-qwen38_27b-expert↗

  baseten

• beetle-bilingual-l2-50-simultaneous-b2-fineweb-2b-heb-eng↗

  Beetle-FineWeb-24B-4

• Moss-image-nsfw-uncensored-lora-GGUF↗

  botp

• CSTinyLlama-1.2B↗

  BUT-FIT

• camie-tagger-v2↗

  Camais03

• nli-distilroberta-base↗

  cross-encoder

  This model was trained using SentenceTransformers Cross-Encoder class. The model was trained on the SNLI and MultiNLI datasets. For a given sentence pair, it will output three scores corresponding to the labels: contradiction, entailment, neutral. For evaluation results, see SBERT.net - Pretrained Cross-Encoder.

• FluxiIA.Fluxi_Think-Small-GGUF↗

  csabakecskemeti

• Qwen3.8-27B-uncensored-heretic-w4a8-ComfyUI↗

  DarkRomeo88

• Qwen3.8-27B-Heretic-GGUF↗

  darrellbest

  GGUF builds of darrellbest/Qwen3.8-27B-Heretic: Qwen3.8-27B with its refusal behaviour removed (0/100 refusals vs 98/100 for the original, KL divergence 0.0465). The three model files are text-only: this is a vision-language model, and llama.cpp keeps the vision encoder in the separate `mmproj` file. Add it only if you want to send images. The included `Modelfile` sets `RENDERER qwen3.8` and `PARSER qwen3.5`, Ollama's built-in support for this architecture. They matter: without them the model's reasoning text ends up inside the answer, and generation was measured about half as fast (9.5 vs 18.8 tokens/s on an RTX PRO 6000 with Q4_K_M).