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AI 模型 84759

• speechless-zephyr-code-functionary-7b-i1-GGUF↗

  mradermacher

• StellarDong-72b-i1-GGUF↗

  mradermacher

• StormSeeker-24B-v1-i1-GGUF↗

  mradermacher

• SuperCorrect-7B-i1-GGUF↗

  mradermacher

• T-VisStar-7B-v0.1-i1-GGUF↗

  mradermacher

• TherapyBeagle-11B-v2-GGUF↗

  mradermacher

• Yi-blossom-40B-GGUF↗

  mradermacher

• Qwen3.8-27B-OBLITERATED-AWQ-W4A16↗

  MurtazaNasir

• OLMo-2-1124-7B-Instruct_SFT_mathfisher_v00.02↗

  Neelectric

• quality-classifier-deberta↗

  NVIDIA

  This is a text classification model that can enable qualitative data annotation, creation of quality-specific blends and addition of metadata tags. The model classifies documents into one of three classes based on the quality of the document: "High", "Medium", "Low" The model was trained using data annotated by human annotators, who considered quality factors such as content accuracy, clarity, coherence, grammar, depth of information, and overall usefulness of the document. This model is used in the NVIDIA NeMo Curator as part of the qualitative filtering module. The model architecture is Deberta V3 Base Context length is 1024 tokens The training set is 22828 Common Crawl text samples, labeled as "High", "Medium", "Low". Here are some examples: 1. Input: