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AI 모델 84759

• functionary-small-v3.2↗

  meetkai

  The model determines when to execute functions, whether in parallel or serially, and can understand their outputs. It only triggers functions as needed. Function definitions are given as JSON Schema Objects, similar to OpenAI GPT function calls. We provide custom code for parsing raw model responses into a JSON object containing `role`, `content` and `tool_calls` fields. This enables the users to read the function-calling output of the model easily. We convert function definitions to a similar text to TypeScript definitions. Then we inject these definitions as system prompts. After that, we inject the default system prompt. Then we start the conversation messages.

• BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext↗

  Microsoft

  * This model was previously named **"PubMedBERT (abstracts + full text)"**. * You can either adopt the new model name "microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext" or update your `transformers` library to version 4.22+ if you need to refer to the old name. Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on general domain corpora, such as newswire and Web. A prevailing assumption is that even domain-specific pretraining can benefit by starting from general-domain language models. Recent work shows that for domains with abundant unlabeled text, such as biomedicine, pretraining language models from scratch results in substantial gains over continual pretraining of general-domain language models. BiomedBERT is pretrained from scratch using _abstracts_ from PubMed and _full-text_ articles from PubMedCentral. This model achieves state-of-the-art performance on many biomedical NLP tasks, and currently holds the top score on the Biomedical Language Understanding and Reasoning Benchmark.

• markuplm-base↗

  Microsoft

  MarkupLM is a simple but effective multi-modal pre-training method of text and markup language for visually-rich document understanding and information extraction tasks, such as webpage QA and webpage information extraction. MarkupLM archives the SOTA results on multiple datasets. For more details, please refer to our paper: MarkupLM: Pre-training of Text and Markup Language for Visually-rich Document Understanding Junlong Li, Yiheng Xu, Lei Cui, Furu Wei, ACL 2022

• Magistral Small 1↗

  Mistral AI

• replit-code-1.5↗

  modularai

• airoboros-65b-gpt4-1.4.1-PI-8192-fp16-i1-GGUF↗

  mradermacher

• ALIA-es-legal-administrative-7B-Instruct-GGUF↗

  mradermacher

• Dark-Pane-i1-GGUF↗

  mradermacher

• Dark-Science-12B-v0.420-i1-GGUF↗

  mradermacher

• Fett-uccine-Long-Noodle-7B-120k-Context-i1-GGUF↗

  mradermacher