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AptAvatar

TaoLiveAIGC

**Links are placeholders for now and will be updated as project pages, model cards, community channels, and reports become available.** - **Endpoint-Anchored Distribution Distillation**: It augments vanilla distribution matching with a dedicated Anchor Score Estimator trained on the trajectory-endpoint distribution defined from a frozen pretrained 4-step bridge generator. This provides an attainable endpoint-level anchor for the evolving two-step student. - **Self-Generated History Replay**: It reuses cached outputs from earlier generator checkpoints as history conditions during chunk-wise training. This approximates inference-time self-conditioning, mitigating quality degradation from accumulated history errors - **2026.06.29** - Released the initial AptAvatar repository with open-source inference code.

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TaoMate-H3

TaoLiveAIGC

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Video generation
Input Text→ Output Video
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TBDub

TaoLiveAIGC

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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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.

Text generation
Input Text→ Output Text
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TLive-Omni-9B

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. 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. We evaluate TLive-Omni-4B and TLive-Omni-9B on both live-commerce tasks and general benchmarks. Dash (-) denotes an unreported result or undisclosed parameter count. The Best results among the compared open-source models are marked in **bold**, while the second-best results are in underlined .

Text generation
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