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AI 模型 84759

• clef-omni↗

  Cloudflare

  Clef-Omni is a 30B-A3B mixture-of-experts multimodal model that turns a state and a schema of typed questions into decisions. It reads the state as text, JSON, images, audio, or video, and returns a probability for every allowed option of every question in a single forward pass. There is no free-form text generation and no output parsing. Clef-Omni is post-trained from Qwen/Qwen3-Omni-30B-A3B-Instruct. See Clef and Clef-Flash for the dense image and video variants. Tested with `torch` 2.11 and `transformers` 5.10.2 on a single H200; the backbone needs about 64 GB of GPU memory in bfloat16. Image inputs also need `pillow`, and audio and video files need `av`.

• quora-distilroberta-base↗

  cross-encoder

  This model was trained on the Quora Duplicate Questions dataset. The model will predict a score between 0 and 1 how likely the two given questions are duplicates. Note: The model is not suitable to estimate the similarity of questions, e.g. the two questions "How to learn Java" and "How to learn Python" will result in a rather low score, as these are not duplicates. You can use this model also without sentence_transformers and by just using Transformers ``AutoModel`` class

• cerebras.Qwen3-Coder-REAP-25B-A3B-GGUF↗

  csabakecskemeti

• mt3-GGUF↗

  cstr

• ppo-Huggy↗

  dgodderis

• WeMM-Embedding-2B-GGUF↗

  DreamBlooms

• distilbert-NER↗

  dslim

  If my open source models have been useful to you, please consider supporting me in building small, useful AI models for everyone (and help me afford med school / help out my parents financially). Thanks! **distilbert-NER** is the fine-tuned version of **DistilBERT**, which is a distilled variant of the BERT model. DistilBERT has fewer parameters than BERT, making it smaller, faster, and more efficient. distilbert-NER is specifically fine-tuned for the task of **Named Entity Recognition (NER)**. This model accurately identifies the same four types of entities as its BERT counterparts: location (LOC), organizations (ORG), person (PER), and Miscellaneous (MISC). Although it is a more compact model, distilbert-NER demonstrates a robust performance in NER tasks, balancing between size, speed, and accuracy.

• esm2_t6_8M_UR50D↗

  facebook

  ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the accompanying paper. You may also be interested in some demo notebooks (PyTorch, TensorFlow) which demonstrate how to fine-tune ESM-2 models on your tasks of interest. Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train:

• mcontriever-msmarco↗

  facebook

• metaclip-2-worldwide-b16-384↗

  facebook