Florence-2-DocVQA
HuggingFaceM4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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idefics-80b-instruct
HuggingFaceM4IDEFICS (**I**mage-aware **D**ecoder **E**nhanced à la **F**lamingo with **I**nterleaved **C**ross-attention**S**) is an open-access reproduction of Flamingo, a closed-source visual language model developed by Deepmind. Like GPT-4, the multimodal model accepts arbitrary sequences of image and text inputs and produces text outputs. IDEFICS is built solely on publicly available data and models. The model can answer questions about images, describe visual contents, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. IDEFICS is on par with the original closed-source model on various image-text benchmarks, including visual question answering (open-ended and multiple choice), image captioning, and image classification when evaluated with in-context few-shot learning. It comes into two variants: a large 80 billion parameters version and a 9 billion parameters version.
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View details ↗idefics-9b
HuggingFaceM4IDEFICS (**I**mage-aware **D**ecoder **E**nhanced à la **F**lamingo with **I**nterleaved **C**ross-attention**S**) is an open-access reproduction of Flamingo, a closed-source visual language model developed by Deepmind. Like GPT-4, the multimodal model accepts arbitrary sequences of image and text inputs and produces text outputs. IDEFICS is built solely on publicly available data and models. The model can answer questions about images, describe visual contents, create stories grounded on multiple images, or simply behave as a pure language model without visual inputs. IDEFICS is on par with the original closed-source model on various image-text benchmarks, including visual question answering (open-ended and multiple choice), image captioning, and image classification when evaluated with in-context few-shot learning. It comes into two variants: a large 80 billion parameters version and a 9 billion parameters version.
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View details ↗idefics-9b-instruct
HuggingFaceM4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗idefics2-8b
HuggingFaceM4> [!WARNING] > Idefics2 will NOT work with `Transformers` version between 4.41.0 and 4.43.3 included. See the issue https://github.com/huggingface/transformers/issues/32271 and the fix https://github.com/huggingface/transformers/pull/32275 Idefics2 is an open multimodal 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. We release under the Apache 2.0 license 2 checkpoints: - idefics2-8b-base: the base model - idefics2-8b: the base model fine-tuned on a mixture of supervised and instruction datasets (text-only and multimodal datasets) - idefics2-8b-chatty: `idefics2-8b` further fine-tuned on long conversation
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View details ↗idefics2-8b-base
HuggingFaceM4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗idefics2-8b-chatty
HuggingFaceM4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Idefics3-8B-Llama3
HuggingFaceM4Idefics3 is an open multimodal 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 and Idefics2, significantly enhancing capabilities around OCR, document understanding and visual reasoning. - **Developed by:** Hugging Face - **Model type:** Multi-modal model (image+text) - **Language(s) (NLP):** en - **License:** Apache 2.0 - **Parent Models:** google/siglip-so400m-patch14-384 and meta-llama/Meta-Llama-3.1-8B-Instruct - **Resources for more information:** - Idefics1 paper: OBELICS: An Open Web-Scale Filtered Dataset of Interleaved Image-Text Documents - Idefics2 paper: What matters when building vision-language models? - Idefics3 paper: Building and better understanding vision-language models: insights and future directions `Idefics3-8B` can be used to perform inference on multimodal (image + text) tasks in which the input is composed of a text query along with one (or multiple) image(s). Text and images can be arbitrarily interleaved. That includes image captioning, visual question answering, etc. These model does not support image generation.
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View details ↗tiny-random-idefics
HuggingFaceM4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗tiny-random-LlamaForCausalLM
HuggingFaceM4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗tiny-random-MistralForCausalLM
HuggingFaceM4基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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