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AI Models 85003

• sup-simcse-ja-base↗

  cl-nagoya

• communicative-baby-rfolmo_score↗

  CLAUSE-Bielefeld

• qnli-distilroberta-base↗

  cross-encoder

  Given a question and paragraph, can the question be answered by the paragraph? The models have been trained on the GLUE QNLI dataset, which transformed the SQuAD dataset into an NLI task. For performance results of this model, see [SBERT.net Pre-trained Cross-Encoder][https://www.sbert.net/docs/pretrained_cross-encoders.html]. You can use the model also directly with Transformers library (without SentenceTransformers library):

• L3-Dark-Planet-8B-NEO-Imatrix-GGUF↗

  DavidAU

  This model has been designed to be relatively bullet proof and operates with all parameters, including temp settings from 0 to 5. It is an extraordinary compressed model, with a very low perplexity level (lower than Meta Llama3 Instruct). Note the "float32" version of this model behaves VERY differently which is why it was not uploaded first. Usually I would use the "float32" version only, however the "character range" displayed by the Bfloat16 and Float32 versions of this model dictate they have their own repos.

• DynamicMind-MoE-Instruct↗

  DedeProGames

• convnextv2-large-22k-384↗

  facebook

• mms-tts-vie↗

  facebook

  This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.

• Qwen3.5-0.8B-heretic-GGUF↗

  FadedRedStar

• Mistral-7B-v0.1↗

  Featherless-Chat-Models

• haphazardlyinc-Andy-Feather-V2-700m-GGUF↗

  Flexan