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AI モデル 84755

• Kimi-K3-Uncensored-GGUF↗

  Ryanchen911

• Qwen3-VL-8B-Instruct-heretic↗

  saidutta69

• parrots-chinese-roberta-wwm-ext-large↗

  shibing624

• stanza-da↗

  stanfordnlp

• TLive-Omni-4B↗

  TaoLiveAIGC

  TLive-Omni is an omni-modal understanding model for e-commerce live-stream, mapping image, video, audio, and text into a unified text-output interface. Built on a Qwen3.5 backbone with a grafted AuT audio encoder, it supports up to 256K tokens of context, trained via a three-stage SFT recipe followed by Faithful-RFT reinforcement fine-tuning. - **Timestamped Per-vGrid layout** — Audio and video tokens are organized into timestamped grid with explicit boundaries, keeping audio segments adjacent to their corresponding visual content for fine-grained temporal alignment over long streams. - **Three-stage SFT recipe** — Progressive training from audio-language alignment to full multimodal SFT, developing live-commerce understanding from omni-modal perception to instruction-following responses. - **Faithful-RFT** — A reinforcement fine-tuning stage for faithful and real-time live-stream demands, suppressing explicit reasoning traces and directly optimizing answer quality for live-commerce tasks. - **Rich atomic capabilities** — A scenario-oriented taxonomy covering speech recognition, speaker analysis, product visual grounding, text recognition, temporal grounding, video dense caption, and omni-modal QA, etc, supported by a compact data production engine. - **Strong live-commerce performance with competitive generalization** — 4B and 9B variants demonstrate strong results across live-commerce audio, image, and video tasks, together with excellent generalization on general benchmarks. TLive-Omni is built on a Qwen3.5 backbone and extends it with a audio encoder through a lightweight MLP aligner, forming a unified text-output omni-modal understanding model. For video inputs with audio, each temporal grid is organized into a timestamped grid that interleaves video and audio token blocks, keeping audio segments adjacent to their corresponding visual content. The model supports up to 256K tokens of context at inference.

• wav2vec2-quran-phonetics↗

  TBOGamer22

  **Quranic Wav2Vec2 Phonetic ASR** is a fine-tuned `wav2vec2` model designed specifically for **phonetic transcription of Quranic recitation**. Unlike standard Arabic ASR systems that output orthographic Arabic text, this model outputs a **phonetic (sound-level) representation**, making it suitable for: - Tajweed research and education - Word-level pronunciation analysis - Forced alignment of Quranic recitation - Linguistic studies of Classical Arabic phonetics

• Qwen3.8-27B-oQ2↗

  TensorFold

• claude2-alpaca-13B-GGUF↗

  TheBloke

• UnslopNemo-12B-v4-GGUF↗

  TheDrummer

• efficientformerv2_s0.snap_dist_in1k↗

  timm