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Image Text To Text

Its take as Input as Text and Image and Transforms to Text.

TipoOtherModelo baseHuggingFaceM4/idefics-9bVersiónRevision ab96ef6Bcalidad

Fuente del modelo

Tipo
Other
Tamaño del archivo
75,5 MB
Modelo base
HuggingFaceM4/idefics-9b
Versión
vRevision ab96ef6
Actualizado
6 oct 2026
Descargas
13
Fuente
Hugging Face

Descripción de la fuente

Model Card for Model ID

Its a Pretrained MultiModal from HuggingFaceM4/idefics2-8b model that accepts arbitrary sequences of image and text inputs and produces text outputs. The model can answer questions about images, describe visual content, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. It improves upon Idefics1, significantly enhancing capabilities around OCR, document understanding and visual reasoning.

Model Description

Its take as Input as Text and Image and Transforms to Text.

  • Developed by: Samim Kumar Patel
  • Model type: Multi-modal model (image+text)
  • Language(s) (NLP): en
  • License: creativeml-openrail-m
  • Finetuned from model [optional]: HuggingFaceM4/idefics2-8b

Use Cases

Healthcare Diagnostics:

Using patient medical records and radiographic images, This Model could assist in providing preliminary diagnoses by cross-referencing symptoms (text) with scan images.

Social Media Moderation:

By analyzing textual posts along with associated images, This Model could help identify and flag inappropriate content or misinformation spread across social media platforms.

Retail Customer Experience:

In retail, This Model can enhance the shopping experience by providing product recommendations through analyzing customer reviews (text) and product images.

Autonomous Vehicles:

This Model could be employed in the development of smarter autonomous driving systems that interpret road signs (text) and detect traffic signals or potential hazards (image).

Educational Tools:

For educational software, This Model could offer more interactive learning experiences by correlating educational content (text) with relevant diagrams or illustrations (image).

Search Engines Optimization:

This Model could revolutionize image-based search engines by improving the accuracy of search results, pairing text queries with visual data to provide more relevant results.

Fuentes

1 fuente
Hugging FacePrincipalVersión ab96ef63961b0d1d57cf4ca98a8315b9e4e0bb04
huggingface.co/samim2024/Image-Text-To-Text/tree/ab96ef63961b0d1d57cf4ca98a8315b9e4e0bb04

Verificado 6 oct

Artefactos del modelo

1 artefacto

adapter_model.safetensors

safetensors · 75,5 MB · SHA-256 2af3c27d2f07…14b1 · Hugging Face

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Extractos de fuentes

3 extractos

--- base_model: HuggingFaceM4/idefics-9b license: creativeml-openrail-m language: - en pipeline_tag: image-text-to-text tags: - multimodal - vision - image-text-to-text library_name: transformers --- # Model Card for Model ID # Its a Pretrained MultiModal from HuggingFaceM4/idefics2-8b model that accepts arbitrary sequences of image and text inputs and produces text outputs. The model can answer questions about images, describe visual content, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. It improves upon [Idefics1](https://huggingface.co/HuggingFaceM4/idefics-80b-instruct), significantly enhancing capabilities around OCR, document understanding and visual reasoning. ### Model Description Its take as Input as Text and Image and Transforms to Text. - **Developed by:** Samim Kumar Patel - **Model type:** Multi-modal model (image+text) - **Language(s) (NLP):** en - **License:** creativeml-openrail-m - **Finetuned from model [optional]:** HuggingFaceM4/idefics2-8b ### Use Cases ### Healthcare Diagnostics: Using patient medical records and radiographic images, This Model could assist in providing preliminary diagnoses by cross-referencing symptoms (text) with scan images. ### Social Media Moderation: By analyzing textual posts along with associated images, This Model could help identify and flag inappropriate content or misinformation spread across social media platforms. ### Retail Customer Experience: In retail, This Model can enhance the shopping experience by providing product recommendations through analyzing customer reviews (text) and product images. ### Autonomous Vehicles: This Model could be employed in the development of smarter autonomous driving systems that interpret road signs (text) and detect traffic signals or potential hazards (image). ### Educational Tools: For educational software, This Model could offer more interactive learning experiences by correlating educational content (text) with relevant diagrams or illustrations (image). ### Search Engines Optimization: This Model could revolutionize image-based search engines by improving the accuracy of search results, pairing text queries with visual data to provide more relevant results.

Source context: 13 downloads · 1 likes · Pipeline image-text-to-text · Library transformers · Repo samim2024/Image-Text-To-Text

Source context: 12 downloads · 1 likes · Pipeline image-text-to-text · Library transformers · Repo samim2024/Image-Text-To-Text