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--- |
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inference: false |
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pipeline_tag: image-text-to-text |
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datasets: |
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- yifanzhang114/SMR |
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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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# SliME Model Card |
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## Model details |
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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: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) |
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**Paper or resources for more information:** |
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Paper: https://huggingface.co/papers/2406.08487 |
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Arxiv: https://arxiv.org/abs/2406.08487 |
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Code: https://github.com/yfzhang114/SliME |
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## License |
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Llama 3 is licensed under the LLAMA 3 Community License, |
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Copyright (c) Meta Platforms, Inc. All Rights Reserved. |
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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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## 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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**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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## Training dataset |
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- SharedGPT4v sft data |
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- SMR data |
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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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