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AI 모델 84760

• Qwen3.5-9B-TAP-DPQ-v7-GGUF↗

  DuoNeural

• OxCoder-9B-GMCE↗

  emperorofrome

• aiss-modern-bert-aidev-pop↗

  evamxb

• IgBert_unpaired↗

  Exscientia

  Model pretrained on protein and antibody sequences using a masked language modeling (MLM) objective. It was introduced in the paper Large scale paired antibody language models. The model is finetuned from ProtBert-BFD using unpaired antibody sequences from the Observed Antibody Space. Note that the tokeniser adds a `[CLS]` token at the beginning of each sequence, a `[SEP]` token at the end of each sequence and pads using the `[PAD]` token. For example a batch containing sequences `E V V M`, `A L` will be tokenised to `[CLS] E V V M [SEP]` and `[CLS] A L [SEP] [PAD] [PAD]`.

• metaclip-h14-fullcc2.5b↗

  facebook

  MetaCLIP model applied to 2.5 billion data points of CommonCrawl (CC). It was introduced in the paper Demystifying CLIP Data by Xu et al. and first released in this repository. Disclaimer: The team releasing MetaCLIP did not write a model card for this model so this model card has been written by the Hugging Face team. The Demystifying CLIP Data paper aims to reveal CLIP’s method around training data curation. OpenAI never open-sourced code regarding their data preparation pipeline.

• mms-tts-bfy↗

  facebook

• mms-tts-bzh↗

  facebook

• PhysicsLM4.2__Llama-3B-Nemo-1T-lr0.003↗

  facebook

• tts_transformer-ar-cv7↗

  facebook

• wav2vec2-base-de-voxpopuli-v2↗

  facebook