patrickamadeus/Qwen2.5-VL-3B-Instruct-ft_lang
patrickamadeus/Qwen2.5-VL-3B-Instruct-ft_lang is a 3.09 billion parameter multimodal instruction-tuned model based on the Qwen2.5-VL architecture, fine-tuned for language understanding. It supports both text-only and image-plus-text inference, making it suitable for visual question answering and image description tasks. This model is a converted checkpoint from patrickamadeus/qwen2_5vl-select-1000, offering standard Qwen2.5-VL / Transformers API compatibility.
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Overview
This model, patrickamadeus/Qwen2.5-VL-3B-Instruct-ft_lang, is a 3.09 billion parameter multimodal instruction-tuned model built upon the Qwen2.5-VL architecture. It is a converted checkpoint from patrickamadeus/qwen2_5vl-select-1000, designed to be fully compatible with the standard Hugging Face Transformers API for Qwen2.5-VL models.
Key Capabilities
- Multimodal Understanding: Processes both text and image inputs, enabling tasks like visual question answering and image description.
- Instruction Following: Fine-tuned to respond to instructions effectively, making it suitable for interactive applications.
- Standard API Integration: Easily loadable and usable with
transformersandqwen-vl-utils, simplifying development and deployment. - Efficient Inference: With 3.09 billion parameters, it offers a balance between performance and computational efficiency for multimodal tasks.
Good For
- Visual Question Answering (VQA): Answering questions based on provided images.
- Image Captioning: Generating descriptive text for images.
- Multimodal Chatbots: Developing conversational agents that can interpret and respond to both textual and visual cues.
- Research and Development: A base for further fine-tuning on specific multimodal datasets or applications.