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@@ -17,12 +17,12 @@ pipeline_tag: visual-question-answering
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  \[[Paper](https://arxiv.org/abs/2312.14238)\] \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
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- | Model | Date | Download | Note |
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- | ----------------------- | ---------- | ------------------------------------------------------------------------------------ | ---------------------------------- |
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- | InternVL-Chat-V1.5 | 2024.04.18 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5) | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (🔥new)|
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- | InternVL-Chat-V1.2-Plus | 2024.02.21 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-Chinese-V1-2-Plus) | more SFT data and stronger |
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- | InternVL-Chat-V1.2 | 2024.02.11 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-Chinese-V1-2) | scaling up LLM to 34B |
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- | InternVL-Chat-V1.1 | 2024.01.24 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-Chinese-V1-1) | support Chinese and stronger OCR |
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  ## Model Details
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  - **Model Type:** multimodal large language model (MLLM)
@@ -53,7 +53,7 @@ from PIL import Image
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  from transformers import AutoModel, CLIPImageProcessor
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  from transformers import AutoTokenizer
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- path = "OpenGVLab/InternVL-Chat-Chinese-V1-1"
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  # If your GPU has more than 40G memory, you can put the entire model on a single GPU.
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  model = AutoModel.from_pretrained(
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  path,
 
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  \[[Paper](https://arxiv.org/abs/2312.14238)\] \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\] \[[中文解读](https://zhuanlan.zhihu.com/p/675877376)]
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+ | Model | Date | Download | Note |
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+ | ----------------------- | ---------- | --------------------------------------------------------------------------- | ---------------------------------- |
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+ | InternVL-Chat-V1.5 | 2024.04.18 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5) | support 4K image; super strong OCR; Approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc. (🔥new)|
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+ | InternVL-Chat-V1.2-Plus | 2024.02.21 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2-Plus) | more SFT data and stronger |
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+ | InternVL-Chat-V1.2 | 2024.02.11 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-2) | scaling up LLM to 34B |
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+ | InternVL-Chat-V1.1 | 2024.01.24 | 🤗 [HF link](https://huggingface.co/OpenGVLab/InternVL-Chat-V1-1) | support Chinese and stronger OCR |
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  ## Model Details
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  - **Model Type:** multimodal large language model (MLLM)
 
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  from transformers import AutoModel, CLIPImageProcessor
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  from transformers import AutoTokenizer
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+ path = "OpenGVLab/InternVL-Chat-V1-1"
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  # If your GPU has more than 40G memory, you can put the entire model on a single GPU.
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  model = AutoModel.from_pretrained(
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  path,