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README.md
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@@ -24,13 +24,27 @@ We investigate domain adaptation of MLLMs through post-training, focusing on dat
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- [2024/12/7] Released [visual-instruction-synthesizer](https://huggingface.co/AdaptLLM/visual-instruction-synthesizer) used to synthesize task triplets based on image-caption pairs.
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- [2024/12/6] Released AdaMLLM developed from Qwen2-VL-2B and Llama-3.2-11B-Vision: [AdaMLLM-med-2B](https://huggingface.co/AdaptLLM/medicine-Qwen2-VL-2B-Instruct), [AdaMLLM-food-2B](https://huggingface.co/AdaptLLM/food-Qwen2-VL-2B-Instruct), [AdaMLLM-med-11B](https://huggingface.co/AdaptLLM/medicine-Llama-3.2-11B-Vision-Instruct), [AdaMLLM-food-11B](https://huggingface.co/AdaptLLM/food-Llama-3.2-11B-Vision-Instruct),
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- [2024/12/05] Released [biomedicine visual instructions](https://huggingface.co/datasets/AdaptLLM/medicine-visual-instructions) for post-training MLLMs
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- [2024/11/29] Released our paper
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## About
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AdaMLLM represents our latest advancement in building domain-specific foundation models through post-training on synthetic supervised tasks derived from unsupervised contexts.
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</p>
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***************** **Updates** ********************
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- [2024/12/9] Released AdaMLLM developed from llava-next-llama3-8b: [AdaMLLM-med-8B](AdaptLLM/medicine-LLaVA-NeXT-Llama3-8B), [AdaMLLM-food-8B](AdaptLLM/food-LLaVA-NeXT-Llama3-8B).
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- [2024/12/7] Released [visual-instruction-synthesizer](https://huggingface.co/AdaptLLM/visual-instruction-synthesizer) used to synthesize task triplets based on image-caption pairs.
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- [2024/12/6] Released AdaMLLM developed from Qwen2-VL-2B and Llama-3.2-11B-Vision: [AdaMLLM-med-2B](https://huggingface.co/AdaptLLM/medicine-Qwen2-VL-2B-Instruct), [AdaMLLM-food-2B](https://huggingface.co/AdaptLLM/food-Qwen2-VL-2B-Instruct), [AdaMLLM-med-11B](https://huggingface.co/AdaptLLM/medicine-Llama-3.2-11B-Vision-Instruct), [AdaMLLM-food-11B](https://huggingface.co/AdaptLLM/food-Llama-3.2-11B-Vision-Instruct),
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- [2024/12/05] Released [biomedicine visual instructions](https://huggingface.co/datasets/AdaptLLM/medicine-visual-instructions) for post-training MLLMs
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- [2024/11/29] Released our paper
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## Resources
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| Model Name | Repo ID in HF 🤗 | Domain | Base Model | Training Data | Evaluation Benchmark |
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|:----------------------------------------------------------------------------|:--------------------------------------------|:--------------|:-------------------------|:------------------------------------------------------------------------------------------------|-----------------------|
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| [Visual Instruction Synthesizer](https://huggingface.co/AdaptLLM/visual-instruction-synthesizer) | AdaptLLM/visual-instruction-synthesizer | - | open-llava-next-llama3-8b | TBD | - |
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| [AdaMLLM-med-2B](https://huggingface.co/AdaptLLM/medicine-Qwen2-VL-2B-Instruct) | AdaptLLM/medicine-Qwen2-VL-2B-Instruct | Biomedicine | Qwen2-VL-2B-Instruct | [medicine-visual-instructions](https://huggingface.co/datasets/AdaptLLM/medicine-visual-instructions) | TBD |
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| [AdaMLLM-food-2B](https://huggingface.co/AdaptLLM/food-Qwen2-VL-2B-Instruct) | AdaptLLM/food-Qwen2-VL-2B-Instruct | Food | Qwen2-VL-2B-Instruct | TBD | TBD |
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| [AdaMLLM-med-8B](https://huggingface.co/AdaptLLM/medicine-LLaVA-NeXT-Llama3-8B) | AdaptLLM/medicine-LLaVA-NeXT-Llama3-8B | Biomedicine | open-llava-next-llama3-8b | [medicine-visual-instructions](https://huggingface.co/datasets/AdaptLLM/medicine-visual-instructions) | TBD |
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| [AdaMLLM-food-8B](https://huggingface.co/AdaptLLM/food-LLaVA-NeXT-Llama3-8B) |AdaptLLM/food-LLaVA-NeXT-Llama3-8B | Food | open-llava-next-llama3-8b | TBD | TBD |
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| [AdaMLLM-med-11B](https://huggingface.co/AdaptLLM/medicine-Llama-3.2-11B-Vision-Instruct) | AdaptLLM/medicine-Llama-3.2-11B-Vision-Instruct | Biomedicine | Llama-3.2-11B-Vision-Instruct | [medicine-visual-instructions](https://huggingface.co/datasets/AdaptLLM/medicine-visual-instructions) | TBD |
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| [AdaMLLM-food-11B](https://huggingface.co/AdaptLLM/food-Llama-3.2-11B-Vision-Instruct) | AdaptLLM/food-Llama-3.2-11B-Vision-Instruct | Food | Llama-3.2-11B-Vision-Instruct | TBD | TBD |
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## About
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AdaMLLM represents our latest advancement in building domain-specific foundation models through post-training on synthetic supervised tasks derived from unsupervised contexts.
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