🚀 Feature Request: Support for GVE-7B Model Inference

🚀 Feature Request: Support for GVE-7B Model Inference
This issue requests the addition of support for inference using the GVE-7B model developed by Alibaba-NLP.

Describe the feature
The feature is to integrate the necessary components and configurations to enable efficient and accurate inference for the GVE-7B (General-purpose Vision-Language Encoder-Decoder) model. This will allow users to load the model weights, process inputs (likely images and text), and generate outputs (e.g., captions, answers to visual questions) directly within our platform/framework.

Paste any useful information
Model Information/Source:

Hugging Face Model Page: https://huggingface.co/Alibaba-NLP/GVE-7B

Potential Implementation Details (based on model card):

GVE-7B is described as a General-purpose Vision-Language Encoder-Decoder model with 7 billion parameters.

It's a Multi-modal Large Language Model (MLLM).

The model structure likely involves a vision encoder (to process images) and a language model (e.g., based on the Llama architecture) for text generation.

Inference will require handling both image and text inputs and generating text outputs.

Additional context
GVE-7B is a competitive, state-of-the-art MLLM from a major research lab (Alibaba-NLP). Supporting this model will significantly enhance our platform's capabilities in the multimodal domain, providing users with a high-performance option for tasks like:

Visual Question Answering (VQA)

Image Captioning

Multimodal Chat

This integration should prioritize performance optimization for efficient memory usage and fast generation speed.

The issue names no repository files, entry points, or tests. Start by reviewing existing multimodal model integrations and the Alibaba-NLP/GVE-7B Hugging Face model page to determine the required loading and inference interfaces. Done means users can load the model, provide image and text inputs, generate outputs, and meet the stated efficiency goals.