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AI 모델 84757

• LaMini-T5-738M↗

  MBZUAI

• gemma-2b↗

  mhenrichsen

• Florence-2-large-ft↗

  Microsoft

  This Hub repository contains a HuggingFace's `transformers` implementation of Florence-2 model from Microsoft. Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model. Resources and Technical Documentation: + Florence-2 technical report. + Jupyter Notebook for inference and visualization of Florence-2-large model

• wavlm-base-plus↗

  Microsoft

  The base model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli

• xclip-base-patch16-ucf-2-shot↗

  Microsoft

• Voxtral-Mini-3B-2507↗

  Mistral AI

  Voxtral Mini is an enhancement of Ministral 3B, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Learn more about Voxtral in our blog post here and our research paper. Voxtral builds upon Ministral-3B with powerful audio understanding capabilities. - **Dedicated transcription mode**: Voxtral can operate in a pure speech transcription mode to maximize performance. By default, Voxtral automatically predicts the source audio language and transcribes the text accordingly - **Long-form context**: With a 32k token context length, Voxtral handles audios up to 30 minutes for transcription, or 40 minutes for understanding - **Built-in Q&A and summarization**: Supports asking questions directly through audio. Analyze audio and generate structured summaries without the need for separate ASR and language models - **Natively multilingual**: Automatic language detection and state-of-the-art performance in the world’s most widely used languages (English, Spanish, French, Portuguese, Hindi, German, Dutch, Italian) - **Function-calling straight from voice**: Enables direct triggering of backend functions, workflows, or API calls based on spoken user intents - **Highly capable at text**: Retains the text understanding capabilities of its language model backbone, Ministral-3B

• Abel-7B-002-GGUF↗

  mradermacher

• Aitana-7B-S-Instruct-GGUF↗

  mradermacher

• Aligner-Med-i1-GGUF↗

  mradermacher

• ArityFlow-Qwen3-4B-Instruct-2507-RolePlay-i1-GGUF↗

  mradermacher