Good folks at 
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Good folks at 
@Meta
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Meta
 have introduced Chameleon 🦎 (who names these things? 🤷‍♂️)
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 have introduced Chameleon 🦎 (who names these things? 🤷‍♂️)
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Chameleon is an AI model that can work with multiple types of data, like text and images, all at once. 🖼️📝
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Chameleon is an AI model that can work with multiple types of data, like text and images, all at once. 🖼️📝
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Before you start searching, as of this post, the model/code have not been open-sourced nor is there any commitment to open-source... sorry! 🚫🔓
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Before you start searching, as of this post, the model/code have not been open-sourced nor is there any commitment to open-source... sorry! 🚫🔓
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Still, here is the technical stuff:
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Still, here is the technical stuff:
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👉 Challenges with Current Systems:
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👉 Challenges with Current Systems:
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📉 Fragmentation: Current multimodal models are often specialized for either text or image tasks, lacking unified approaches.
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📉 Fragmentation: Current multimodal models are often specialized for either text or image tasks, lacking unified approaches.
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📊 Scalability: Existing systems struggle with scaling to handle complex, mixed-modal tasks without significant performance degradation.
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📊 Scalability: Existing systems struggle with scaling to handle complex, mixed-modal tasks without significant performance degradation.
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🔄 Alignment: Aligning textual and visual modalities remains a technical challenge, often requiring separate processing pipelines.
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🔄 Alignment: Aligning textual and visual modalities remains a technical challenge, often requiring separate processing pipelines.
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👉 Objective:
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👉 Objective:
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🎯 Unified Modeling: Develop a single model capable of handling various multimodal tasks (text generation, image generation, image captioning, visual question answering) seamlessly.
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🎯 Unified Modeling: Develop a single model capable of handling various multimodal tasks (text generation, image generation, image captioning, visual question answering) seamlessly.
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👉 How It's Done 📘
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👉 How It's Done 📘
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Early-Fusion Architecture 🧠: Utilizes an early-fusion token-based approach to integrate text and image data from the beginning.
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Early-Fusion Architecture 🧠: Utilizes an early-fusion token-based approach to integrate text and image data from the beginning.
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Stable Training 💪: Implements a tailored alignment recipe and specific architectural parameterization to ensure stability in mixed-modal settings.
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Stable Training 💪: Implements a tailored alignment recipe and specific architectural parameterization to ensure stability in mixed-modal settings.
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Broad Evaluation 📊: Assesses the model across various tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed-modal generation.
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Broad Evaluation 📊: Assesses the model across various tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed-modal generation.
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👉 Results: (Fun fact they mention Llava-1.5 in comparison but never really share the results)
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👉 Results: (Fun fact they mention Llava-1.5 in comparison but never really share the results)
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🏆 Performance: Chameleon achieves state-of-the-art results in image captioning and outperforms models like Llama-2 in text-only tasks.
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🏆 Performance: Chameleon achieves state-of-the-art results in image captioning and outperforms models like Llama-2 in text-only tasks.
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⚖️ Competitiveness: It shows competitive performance with models such as Mixtral 8x7B and Gemini-Pro.
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⚖️ Competitiveness: It shows competitive performance with models such as Mixtral 8x7B and Gemini-Pro.
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👩‍⚖️ Human Judgments: Matches or exceeds the performance of larger models, including Gemini Pro and GPT-4V
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👩‍⚖️ Human Judgments: Matches or exceeds the performance of larger models, including Gemini Pro and GPT-4V
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Paper:  
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Paper:  
Chameleon: Mixed-Modal Early-Fusion Foundation Models (2405.09818)
https://huggingface.co/papers/2405.09818
2405.09818
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https://huggingface.co/papers/2405.09818
Good folks at @Meta have introduced Chameleon 🦎 (who names these things? 🤷‍♂️)

Chameleon is an AI model that can work with multiple types of data, like text and images, all at once. 🖼️📝

Before you start searching, as of this post, the model/code have not been open-sourced nor is there any commitment to open-source... sorry! 🚫🔓

Still, here is the technical stuff:

👉 Challenges with Current Systems:

📉 Fragmentation: Current multimodal models are often specialized for either text or image tasks, lacking unified approaches.

📊 Scalability: Existing systems struggle with scaling to handle complex, mixed-modal tasks without significant performance degradation.

🔄 Alignment: Aligning textual and visual modalities remains a technical challenge, often requiring separate processing pipelines.

👉 Objective:

🎯 Unified Modeling: Develop a single model capable of handling various multimodal tasks (text generation, image generation, image captioning, visual question answering) seamlessly.

👉 How It's Done 📘

Early-Fusion Architecture 🧠: Utilizes an early-fusion token-based approach to integrate text and image data from the beginning.

Stable Training 💪: Implements a tailored alignment recipe and specific architectural parameterization to ensure stability in mixed-modal settings.

Broad Evaluation 📊: Assesses the model across various tasks, including visual question answering, image captioning, text generation, image generation, and long-form mixed-modal generation.

👉 Results: (Fun fact they mention Llava-1.5 in comparison but never really share the results)

🏆 Performance: Chameleon achieves state-of-the-art results in image captioning and outperforms models like Llama-2 in text-only tasks.

⚖️ Competitiveness: It shows competitive performance with models such as Mixtral 8x7B and Gemini-Pro.

👩‍⚖️ Human Judgments: Matches or exceeds the performance of larger models, including Gemini Pro and GPT-4V

Paper:  https://huggingface.co/papers/2405.09818
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