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AI 模型 84753

• nova-d48w1024-sd512↗

  BAAI

• Hermes-3-Llama-3.1-405B-GGUF↗

  bartowski

• TheDrummer_Skyfall-31B-v4-GGUF↗

  bartowski

• ThinkingCap-NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF↗

  bottlecapai

• Accordion-6B-A0.5B-Base-32K↗

  btzyd

• ltx2.5-model-image-to-video↗

  caohoangkha99

• codesage-large-v2↗

  codesage

  CodeSage is a family of open code embedding models with an encoder architecture that supports a wide range of source code understanding tasks. It was initially introduced in the paper: For this V2 model, we enhanced semantic search performance by improving the quality of the contrastive learning data through consistency filtering. Starting from the pretrained checkpoint (trained with both Masked Language Modeling (MLM) and deobfuscation Section 3.1) from our V1 model training (Zhang et al., 2023), we applied contrastive learning with the filtered data. Unlike the V1 model, we extracted the initial set of (text, code) pairs—specifically, summaries and function/class bodies—from The Stack V2 data instead of using the V1 data. We employed simple rule-based filtering as detailed in our previous work. We then applied consistency filtering to further refine the data. While using The Stack V2 resulted in minor performance boosts on downstream tasks, the majority of the performance improvements came from the consistency filtering. This pretrained checkpoint is the same as those used by our V1 model (codesage/codesage-small, which is trained on The Stack data. The constative learning data are extracted from The Stack V2. Same as our V1 model, we supported nine languages as follows: c, c-sharp, go, java, javascript, typescript, php, python, ruby.

• yolo11x_watermark_detection↗

  corzent

• Tiamat-7b-1.1-DPO-imatrix-GGUF↗

  duyntnet

• MiniCPM5-2B-RotSVDMix-Quantized↗

  ewin-reg