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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ inference: false
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+ pipeline_tag: image-text-to-text
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+ ---
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+
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+ <br>
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+ <br>
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+
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+ <img src="https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/F2d0zMtwUqPKtOrbMu0Gr.jpeg" alt="image/jpeg" style="width:10%;">
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+
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+ # SliME Model Card
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+
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+ ## Model details
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+
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+ **Model type:**
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+ SliME is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.
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+ It is an auto-regressive language model, based on the transformer architecture.
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+ Base LLM: [lmsys/vicuna-13b-v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5)
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/_dsyhwdanIgUPtejamXmX.png)
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+
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+ **Paper or resources for more information:**
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+ https://github.com/yfzhang114/SliME
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+
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+ ## License
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+ Llama 2 is licensed under the LLAMA 2 Community License,
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+ Copyright (c) Meta Platforms, Inc. All Rights Reserved.
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+
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+ **Where to send questions or comments about the model:**
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+ https://github.com/yfzhang114/SliME/issues
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+
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+ ## Intended use
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+ **Primary intended uses:**
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+ The primary use of SliME is research on large multimodal models and chatbots.
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+
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+ **Primary intended users:**
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+ 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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+
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+ ## Training dataset
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+ - SharedGPT4v sft data
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+ - SMR data
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+
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+ ## Evaluation dataset
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+ A collection of 15 benchmarks, including 5 academic VQA benchmarks and 10 recent benchmarks specifically proposed for instruction-following LMMs.
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+
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/dLXygEd23t-xImhSBLlta.png)