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AI 모델 84997

• tiny_OPTForSequenceClassification-lora↗

  peft-internal-testing

• rtdetr_v2_r50vd↗

  PekingU

  The RT-DETRv2 model was proposed in RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection Transformer by Wenyu Lv, Yian Zhao, Qinyao Chang, Kui Huang, Guanzhong Wang, Yi Liu. RT-DETRv2 refines RT-DETR by introducing selective multi-scale feature extraction, a discrete sampling operator for broader deployment compatibility, and improved training strategies like dynamic data augmentation and scale-adaptive hyperparameters. These changes enhance flexibility and practicality while maintaining real-time performance. This model was contributed by @jadechoghari with the help of @cyrilvallez and @qubvel-hf RT-DETRv2 consistently outperforms its predecessor across all model sizes while maintaining the same real-time speeds.

• KnowLine-4B-Gen5.5↗

  PelaAI

• DeepSeek-V4.1-Flash-GSQ-RCO-GGUF↗

  pfeifferj

  > **TL;DR:** DeepSeek's 552B-parameter backbone plus 196B-parameter Engram memory, reduced from the original 510.3 GB FP8/FP4 checkpoint to a 426.1 GB package including the DSpark draft model and vision projector. **Non-uniform GGUF quantization** produced with **GSQ** and **RCO**, with the DSpark draft model and the vision projector. > Independent community reproduction. These files were produced by a third party using the published GSQ and RCO methods. They are not an IST-DASLab release and carry no endorsement from the authors of either paper.

• google_gemma-4-12B-it-ingest-best-gptq-permuted↗

  positron-ai

• gemma-4-E4B-it-GGUF↗

  prithivMLmods

• Flow-Judge-v0.1-GGUF↗

  QuantFactory

• chinese-decision-2b-gguf↗

  quincyqiang

• tyrian-75m↗

  redptam

• mlfoundations-dev_-_OH_DCFT_V3_wo_collective_cognition-gguf↗

  RichardErkhov