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AI 모델 84759

• Nanbeige4.3-33B-FP8↗

  primitive-ai

• Ternary-Bonsai-2-27B-DFlash2↗

  ProCreations

  Updated September 18, 2026 with the best verified continued-training checkpoint: **r2-dflash**. The original Ternary Bonsai 2 27B PQ2_0 target is unchanged. The head is available as BF16 master weights and **integer Q8_0** inference weights, not FP8. Matched testing against the previous release measured **169.12 → 170.04 decode tokens/sec (+0.54%)** on eight unseen prompts with two repeats. This is a small measured gain, not a large speedup. Both arms used the same runtime and maximum 3 draft tokens. Acceptance is accepted draft tokens divided by proposed draft tokens. Throughput is total generated tokens divided by total decode time, including reasoning tokens. The complete comparison links to the preserved raw requests, outputs and counters in the same reports directory.

• voice-activity-detection↗

  pyannote

• deepseek-coder-7b-instruct-v1.5-GGUF↗

  QuantFactory

• Llama3-8B-Chinese-Chat-GGUF↗

  QuantFactory

• MyAlee-Qwen-Instruct-v2-16k-v1-GGUF↗

  QuantFactory

• Llama-3.2-8B-bnb-4bit-POI↗

  Quentin123

• Qwen3.6-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-NEO-CODE-Di-IMatrix-MAX-GGUF↗

  Roma123886

• bengali-whisper-medium-ct2↗

  SayedShaun

• SeedVR2-3B↗

  字节跳动

  > Jianyi Wang, Shanchuan Lin, Zhijie Lin, Yuxi Ren, Meng Wei, Zongsheng Yue, Shangchen Zhou, Hao Chen, Yang Zhao, Ceyuan Yang, Xuefeng Xiao, Chen Change Loy, Lu Jiang > > Recent advances in diffusion-based video restoration (VR) demonstrate significant improvement in visual quality, yet yield a prohibitive computational cost during inference. While several distillation-based approaches have exhibited the potential of one-step image restoration, extending existing approaches to VR remains challenging and underexplored, due to the limited generation ability and poor temporal consistency, particularly when dealing with high-resolution video in real-world settings. In this work, we propose a one-step diffusion-based VR model, termed as SeedVR2, which performs adversarial VR training against real data. To handle the challenging high-resolution VR within a single step, we introduce several enhancements to both model architecture and training procedures. Specifically, an adaptive window attention mechanism is proposed, where the window size is dynamically adjusted to fit the output resolutions, avoiding window inconsistency observed under high-resolution VR using window attention with a predefined window size. To stabilize and improve the adversarial post-training towards VR, we further verify the effectiveness of a series of losses, including a proposed feature matching loss without significantly sacrificing training efficiency. Extensive experiments show that SeedVR2 can achieve comparable or even better performance compared with existing VR approaches in a single step. **Limitations:** These are the prototype models and the performance may not be perfectly align with the paper. Our methods are sometimes not robust to heavy degradations and very large motions, and shares some failure cases with existing methods, e.g., fail to fully remove the degradation or simply generate unpleasing details. Moreover, due to the strong generation ability, Our methods tend to overly generate details on inputs with very light degradations, e.g., 720p AIGC videos, leading to oversharpened results occasionally.