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---
inference: false
pipeline_tag: image-text-to-text
datasets:
- yifanzhang114/SMR
---
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<img src="https://cdn-uploads.huggingface.co/production/uploads/623d8ca4c29adf5ef6175615/F2d0zMtwUqPKtOrbMu0Gr.jpeg" alt="image/jpeg" style="width:10%;">
# SliME Model Card
## Model details
**Model type:**
SliME 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: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
**Paper or resources for more information:**
Paper: https://huggingface.co/papers/2406.08487
Arxiv: https://arxiv.org/abs/2406.08487
Code: https://github.com/yfzhang114/SliME
## License
Llama 3 is licensed under the LLAMA 3 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
**Where to send questions or comments about the model:**
https://github.com/yfzhang114/SliME/issues
## Intended use
**Primary intended uses:**
The primary use of SliME is research on large multimodal models and chatbots.
**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.
## Training dataset
- SharedGPT4v sft data
- SMR data
## Evaluation dataset
A collection of 15 benchmarks, including 5 academic VQA benchmarks and 10 recent benchmarks specifically proposed for instruction-following LMMs.
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