bakLlava-v1-hf
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llama3-llava-next-8b-hf
llava-hf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 ↗llava-1.5-13b-hf
llava-hfBelow is the model card of Llava model 13b, which is copied from the original Llava model card that you can find here. Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1qsl6cd2c8gGtEW1xV5io7S8NHh-Cp1TV?usp=sharing) **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.
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View details ↗llava-1.5-7b-hf
llava-hfBelow is the model card of Llava model 7b, which is copied from the original Llava model card that you can find here. Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1qsl6cd2c8gGtEW1xV5io7S8NHh-Cp1TV?usp=sharing) Or check out our Spaces demo! [](https://huggingface.co/spaces/llava-hf/llava-4bit)
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View details ↗llava-interleave-qwen-0.5b-hf
llava-hf**Model type:** LLaVA Interleave is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: Qwen/Qwen1.5-7B-Chat **Primary intended uses:** The primary use of LLaVA-Next Interleave is research on large multimodal models and chatbots. This is only for research exploration, and prohibited for commercial usage. **Primary intended users:** The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
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View details ↗llava-interleave-qwen-7b-hf
llava-hf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 ↗llava-next-72b-hf
llava-hf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 ↗LLaVA-NeXT-Video-7B-32K-hf
llava-hf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 ↗LLaVA-NeXT-Video-7B-DPO-hf
llava-hf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 ↗LLaVA-NeXT-Video-7B-hf
llava-hfCheck out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1CZggLHrjxMReG-FNOmqSOdi4z7NPq6SO?usp=sharing) Disclaimer: The team releasing LLaVa-NeXT-Video did not write a model card for this model so this model card has been written by the Hugging Face team. **Model type:** LLaVA-Next-Video is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. The model is buit on top of LLaVa-NeXT by tuning on a mix of video and image data to achieve better video understanding capabilities. The videos were sampled uniformly to be 32 frames per clip. The model is a current SOTA among open-source models on VideoMME bench. Base LLM: lmsys/vicuna-7b-v1.5
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View details ↗llava-onevision-qwen2-0.5b-ov-hf
llava-hfCheck out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1-4AtYjR8UMtCALV0AswU1kiNkWCLTALT?usp=sharing) Below is the model card of 0.5B LLaVA-Onevision model which is copied from the original LLaVA-Onevision model card that you can find here. **Model type:** LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data. LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.
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View details ↗llava-onevision-qwen2-0.5b-si-hf
llava-hf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 ↗llava-onevision-qwen2-72b-ov-hf
llava-hf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 ↗llava-onevision-qwen2-7b-ov-hf
llava-hfCheck out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1-4AtYjR8UMtCALV0AswU1kiNkWCLTALT?usp=sharing) Below is the model card of 7B LLaVA-Onevision model which is copied from the original LLaVA-Onevision model card that you can find here. **Model type:** LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data. LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.
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View details ↗llava-onevision-qwen2-7b-si-hf
llava-hf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 ↗llava-v1.6-34b-hf
llava-hf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 ↗llava-v1.6-mistral-7b-hf
llava-hfThe LLaVA-NeXT model was proposed in LLaVA-NeXT: Improved reasoning, OCR, and world knowledge by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee. LLaVa-NeXT (also called LLaVa-1.6) improves upon LLaVa-1.5 by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning. Disclaimer: The team releasing LLaVa-NeXT did not write a model card for this model so this model card has been written by the Hugging Face team. LLaVa combines a pre-trained large language model with a pre-trained vision encoder for multimodal chatbot use cases. LLaVA 1.6 improves on LLaVA 1.5 BY: - Using Mistral-7B (for this checkpoint) and Nous-Hermes-2-Yi-34B which has better commercial licenses, and bilingual support - More diverse and high quality data mixture - Dynamic high resolution
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View details ↗llava-v1.6-vicuna-13b-hf
llava-hf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 ↗llava-v1.6-vicuna-7b-hf
llava-hfThe LLaVA-NeXT model was proposed in LLaVA-NeXT: Improved reasoning, OCR, and world knowledge by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee. LLaVa-NeXT (also called LLaVa-1.6) improves upon LLaVa-1.5 by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning. Disclaimer: The team releasing LLaVa-NeXT did not write a model card for this model so this model card has been written by the Hugging Face team. LLaVa combines a pre-trained large language model with a pre-trained vision encoder for multimodal chatbot use cases. LLaVA 1.6 improves on LLaVA 1.5 BY: - More diverse and high quality data mixture - Dynamic high resolution
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View details ↗vip-llava-7b-hf
llava-hfBelow is the model card of VipLlava model 7b, which is copied from the original Llava model card that you can find here. Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance (the model works similarly as Llava): [](https://colab.research.google.com/drive/1-0G7Kuj2iQgKux4NJneP2JefFMamxG6Q?usp=sharing) **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.
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