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AI 모델 85003

• ov-tiny-random-gpt2-with-cache↗

  optimum-intel-internal-testing

  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• segformer_for_optic_disc_cup_segmentation↗

  pamixsun

• VeLO_RNN↗

  Pauljanson002

• tiny-random-DebertaV2Model↗

  peft-internal-testing

  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• google_gemma-4-31B-it-ingest-best-gptq-permuted↗

  positron-ai

• bert-medium-mnli↗

  prajjwal1

  The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the official Google BERT repository. These BERT variants were introduced in the paper Well-Read Students Learn Better: On the Importance of Pre-training Compact Models. These models are trained on MNLI.

• gemma-4-31B-it-Uncensored-MAX-GGUF↗

  prithivMLmods

• Qwen3-1.7B-GGUF↗

  prithivMLmods

• Qwen3-VL-4B-Instruct-Unredacted-MAX-GGUF↗

  prithivMLmods

• EndConvo-health-1b-GGUF-v1↗

  ProdocAI