LLaVA++

LLaVA++ extends the LLaVA model by integrating Phi-3 and LLaMA-3, enhancing the interaction capability between visual and language models.

CommonProductProgrammingArtificial IntelligenceNatural Language Processing

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LLaVA++ is an open-source project aiming to extend the visual capabilities of the LLaVA model by integrating Phi-3 and LLaMA-3 models. Developed by researchers at Mohamed bin Zayed University of AI (MBZUAI), the project enhances the model's performance on instruction-following and task-oriented datasets by combining the latest large language models.

Integrates Phi-3 Mini Instruct and LLaMA-3 Instruct models to enhance language understanding capabilities.
Has undergone performance comparisons on multiple benchmarks and datasets
demonstrating the model's advantages.
Provides pre-trained models and LoRA weighted fine-tuning models to accommodate different usage scenarios.
Offers an interactive chat experience through Google Colab.
Supports pre-training and fine-tuning of the model to optimize performance for specific tasks.
Detailed installation and training instructions are provided for the convenience of researchers and developers.

Researchers and developers can use LLaVA++ for the study and development of language models.
It is suitable for commercial applications that require language understanding and generation tasks.
In the educational field
the model can be used for language teaching and research.
It is of great significance for exploring the application of artificial intelligence in the combined field of vision and language.

In the educational field
LLaVA++ can be used to assist language learning and provide accurate language understanding and generation.
In commercial applications
LLaVA++ can be integrated to enhance the intelligence level of customer service systems.
Research institutions can use LLaVA++ for academic research on language models and publish related papers.

Step 1: Visit the GitHub project page to clone or download the LLaVA++ codebase.
Step 2: Follow the project's installation guide to update necessary dependencies by running the provided scripts.
Step 3: Choose a pre-trained model or fine-tune the model to meet specific application scenarios as needed.
Step 4: Experience the interactive chat feature of the model using the provided Google Colab link.
Step 5: According to the project's documentation and guidelines

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Bounce Rate

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LLaVA++

LLaVA++ Visit Over Time

LLaVA++ Visit Trend

LLaVA++ Visit Geography

LLaVA++ Traffic Sources

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C4AI Command R+ 08-2024 — A multilingual advanced dialogue generation model with 104 billion parameters.

LLM Playground — An interactive platform for loading and testing large language models.

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