llava-llama-3-8b-v1_1

A LLaVA model optimized by XTuner, which combines image and text processing capabilities.

PremiumNewProductProgrammingArtificial IntelligenceMultimodal Learning

llava-llama-3-8b-v1_1 is an optimized LLaVA model by XTuner, based on meta-llama/Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336. It has been fine-tuned with ShareGPT4V-PT and InternVL-SFT. Designed for the combination of image and text processing, the model features strong multimodal learning capabilities and is suitable for various downstream deployment and evaluation toolkits.

Multimodal Learning: Combines text and image processing abilities
capable of understanding and generating text related to images.
Efficient Fine-tuning: Fine-tuned by ShareGPT4V-PT and InternVL-SFT to improve the model's adaptability and accuracy.
High Compatibility: Compatible with multiple downstream deployment and evaluation toolkits
facilitating integration and usage.
Large-scale Parameters: boasting 8.03B parameters
providing powerful model performance.
High Accuracy Results: Achieved excellent results on multiple evaluation metrics
such as 72.3% and 66.4%.
Support for FP16: The model supports FP16 precision

Data Scientist: Conduct deep learning research integrating image and text.
Machine Learning Engineer: Build and deploy multimodal learning models to address practical problems.
Researcher: Explore and experiment with the potentials and applications of multimodal artificial intelligence.

Used for image annotation and description generation to improve the accuracy of image search.
In social media analysis
combined image and text content for sentiment analysis.
As a backend for a chatbot
providing a richer user interaction experience.

1. Install the required libraries and dependencies
ensuring the environment supports model execution.
2. Load the llava-llama-3-8b-v1_1 model from Hugging Face.
3. Prepare input data
including images and relevant text.

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Monthly Visits

20899836

Bounce Rate

46.04%

Page per Visit

5.2

Visit Duration

00:04:57

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