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AI Models 84755

• Qwen_Qwen3.5-27B-GGUF↗

  bartowski

  All quants made using imatrix option with dataset from here - llama.cpp - ramalama - LM Studio - koboldcpp - Jan AI - Text Generation Web UI - LoLLMs Note: if it's a newly supported model, you may need to wait for an update from the developers.

• Qwen3.8-Flash-Next-UNCENSORED-IQ2_XXS-GGUF↗

  BattleGhost

  IQ2_XXS GGUF conversion of `dealignai/Qwen3.8-Flash-Next-UNCENSORED-FP8`, intended as a practical build for a system with 64 GB RAM and a 16 GB GPU. This is the exact modified **dealignai UNCENSORED** checkpoint converted and quantized to GGUF. It is not derived from the independently modified OrcaRouter model. The unquantized BF16 GGUF is available separately at `BattleGhost/Qwen3.8-Flash-Next-UNCENSORED-BF16-GGUF`.

• MOSS-TTS-v1.5-GGUF↗

  bombman

• SapBERT-from-roberta-base-biomedical-clinical-es↗

  BSC-NLP4BIA

• whistle↗

  Cactus-Compute

  A speech-to-text model for mobiles, wearables, robots, smart home, automotive and microcontrollers. The whole model is a single 16.9 MB file, and it runs on the same CPU engine as Needle, from the same container and the same quantisation, with no dependencies and no GPU. - **Transcription**: 16 kHz mono audio of any length, one pass up to 30 seconds and windowed beyond it, in English, German, French, Spanish, Italian, Dutch and Polish. The language is detected unless you name it, and silence and steady noise return an empty transcript rather than an invented sentence; other sounds, music especially, can still come out as text, so put a voice activity detector or a wake word in front of an always-on microphone. Live audio streams for as long as you like, committing the words two passes agree on. - **Word timestamps**: every word with its start, end and probability, aligned from the decoder's own attention, so an app can highlight, seek or cut on a word. - **Speech embedding**: the encoder output, one row per 80 ms frame, for matching and retrieval without decoding a transcript at all. Keyword biasing takes the names, places, product words and jargon your users actually say and favours them during the search, so the ones that matter survive.

• ruri-large↗

  cl-nagoya

• clef-omni↗

  Cloudflare

  Clef-Omni is a 30B-A3B mixture-of-experts multimodal model that turns a state and a schema of typed questions into decisions. It reads the state as text, JSON, images, audio, or video, and returns a probability for every allowed option of every question in a single forward pass. There is no free-form text generation and no output parsing. Clef-Omni is post-trained from Qwen/Qwen3-Omni-30B-A3B-Instruct. See Clef and Clef-Flash for the dense image and video variants. Tested with `torch` 2.11 and `transformers` 5.10.2 on a single H200; the backbone needs about 64 GB of GPU memory in bfloat16. Image inputs also need `pillow`, and audio and video files need `av`.

• quora-distilroberta-base↗

  cross-encoder

  This model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates. Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rather low score, as these are not duplicates. You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class

• cerebras.Qwen3-Coder-REAP-25B-A3B-GGUF↗

  csabakecskemeti

• mt3-GGUF↗

  cstr