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AI モデル 84759

• SpatialAxiom_SpatialAxiom-9B-GGUF↗

  bartowski

• Gemma-4-E4B-it-NVFP4↗

  bg-digitalservices

• ltxv-gguf↗

  biali

• bluebert_pubmed_mimic_uncased_L-12_H-768_A-12↗

  bionlp

  We provide preprocessed PubMed texts that were used to pre-train the BlueBERT models. The corpus contains ~4000M words extracted from the PubMed ASCII code version. This work was supported by the Intramural Research Programs of the National Institutes of Health, National Library of Medicine and Clinical Center. This work was supported by the National Library of Medicine of the National Institutes of Health under award number 4R00LM013001-01. This tool shows the results of research conducted in the Computational Biology Branch, NCBI. The information produced on this website is not intended for direct diagnostic use or medical decision-making without review and oversight by a clinical professional. Individuals should not change their health behavior solely on the basis of information produced on this website. NIH does not independently verify the validity or utility of the information produced by this tool. If you have questions about the information produced on this website, please see a health care professional. More information about NCBI's disclaimer policy is available.

• storyboard-sketch↗

  blink7630

• salamandra-2b↗

  BSC-LT

• rubert-tiny2-russe-toxicity↗

  BunnyNoBugs

  This model is a fine-tuned version of cointegrated/rubert-tiny2 on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2087 - Precision Macro: 0.9395 - Recall Macro: 0.9394 - F1 Macro: 0.9394 - F1 Neutral: 0.9398 - F1 Toxic: 0.9390 The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10

• roberta-base-indonesian-1.5G↗

  cahya

• audio.cpp-gguf↗

  CaptainArni

  GGUF conversions of music and speech models for audio.cpp, the ggml-based native audio inference framework. \* proposed in audio.cpp#464; until that lands, download the file and point `--model` at its directory. The XL DiT is the larger ACE-Step 1.5 transformer: 32 layers of 2560 against turbo's 24 of 2048, with 32 attention heads of 128. Each file is self-contained the way audio.cpp's other ACE-Step GGUFs are — XL DiT, planner LM, text encoder and VAE in one file — so none of them needs another download.

• gemma-4-E4B-it-OBLITERATED-Q8_0-GGUF↗

  Centara