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InternVL2-26B

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Transformers
Safetensors
multilingual
internvl_chat
feature-extraction
internvl
custom_code
conversational
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@@ -23,26 +23,27 @@ InternVL 2.0 is a multimodal large language model series, featuring models of va
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  ## Performance
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- | Benchmark | MiniCPM-Llama3-V-2_5 | InternVL-Chat-V1-5 | InternVL2-26B |
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- | :--------------------------: | :------------------: | :----------------: | :-----------: |
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- | Model Size | 8.5B | 25.5B | 25.5B |
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- | | | | |
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- | DocVQA<sub>test</sub> | 84.8 | 90.9 | |
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- | ChartQA<sub>test</sub> | - | 83.8 | |
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- | InfoVQA<sub>test</sub> | - | 72.5 | |
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- | TextVQA<sub>val</sub> | 76.6 | 80.6 | |
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- | OCRBench | 725 | 724 | |
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- | MME<sub>sum</sub> | 2024.6 | 2187.8 | |
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- | RealWorldQA | 63.5 | 66.0 | |
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- | AI2D<sub>test</sub> | 78.4 | 80.7 | |
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- | MMMU<sub>val</sub> | 45.8 | 45.2 | |
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- | MMBench-EN<sub>test</sub> | 77.2 | 82.2 | |
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- | MMBench-CN<sub>test</sub> | 74.2 | 82.0 | |
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- | CCBench<sub>dev</sub> | 45.9 | 69.8 | |
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- | MMVet<sub>GPT-4-0613</sub> | - | 62.8 | |
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- | SEED-Image | 72.3 | 76.0 | |
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- | HallBench<sub>avg</sub> | 42.4 | 49.3 | |
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- | MathVista<sub>testmini</sub> | 54.3 | 53.5 | |
 
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  - We simultaneously use InternVL and VLMEvalKit repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA, TextVQA, MME, AI2D, MMBench, CCBench, MMVet, and SEED-Image were tested using the InternVL repository. MMMU, OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit.
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  ## Performance
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+ | Benchmark | GPT-4T-20240409 | Gemini-1.5-Pro | InternVL-Chat-V1-5 | InternVL2-26B |
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+ | :--------------------------: | :-------------: | :------------: | :----------------: | :-----------: |
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+ | Model Size | - | - | 25.5B | 25.5B |
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+ | | | | | |
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+ | DocVQA<sub>test</sub> | 87.2 | 86.5 | 90.9 | |
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+ | ChartQA<sub>test</sub> | 78.1 | 81.3 | 83.8 | |
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+ | InfoVQA<sub>test</sub> | - | 72.7 | 72.5 | |
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+ | TextVQA<sub>val</sub> | - | 73.5 | 80.6 | |
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+ | OCRBench | 678 | 754 | 724 | |
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+ | MME<sub>sum</sub> | 2070.2 | - | 2187.8 | |
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+ | RealWorldQA | 68.0 | 67.5 | 66.0 | |
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+ | AI2D<sub>test</sub> | 89.4 | 80.3 | 80.7 | |
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+ | MMMU<sub>val</sub> | 63.1 | 58.5 | 45.2 | |
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+ | MMBench-EN<sub>test</sub> | 81.0 | 73.9 | 82.2 | |
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+ | MMBench-CN<sub>test</sub> | 80.2 | 73.8 | 82.0 | |
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+ | CCBench<sub>dev</sub> | 57.3 | 28.4 | 69.8 | |
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+ | MMVet<sub>GPT-4-0613</sub> | - | - | 62.8 | |
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+ | MMVet<sub>GPT-4-Turbo</sub> | 67.5 | 64.0 | 55.4 | |
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+ | SEED-Image | - | - | 76.0 | |
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+ | HallBench<sub>avg</sub> | 43.9 | 45.6 | 49.3 | |
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+ | MathVista<sub>testmini</sub> | 58.1 | 57.7 | 53.5 | |
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  - We simultaneously use InternVL and VLMEvalKit repositories for model evaluation. Specifically, the results reported for DocVQA, ChartQA, InfoVQA, TextVQA, MME, AI2D, MMBench, CCBench, MMVet, and SEED-Image were tested using the InternVL repository. MMMU, OCRBench, RealWorldQA, HallBench, and MathVista were evaluated using the VLMEvalKit.
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