patrickamadeus/Qwen2.5-VL-3B-Instruct-ft
patrickamadeus/Qwen2.5-VL-3B-Instruct-ft is a 3 billion parameter Qwen2.5-VL-based multimodal instruction-tuned model, converted from patrickamadeus/qwen2_5vl-1000. This model supports both text-only and image-plus-text inference, making it suitable for visual question answering and image description tasks. It offers a 32K context length and is designed for direct use with standard Transformers API for multimodal applications.
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Overview
patrickamadeus/Qwen2.5-VL-3B-Instruct-ft is a 3 billion parameter multimodal instruction-tuned model built on the Qwen2.5-VL architecture. It is a converted checkpoint from patrickamadeus/qwen2_5vl-1000, designed for seamless integration with the standard Hugging Face Transformers API without requiring custom wrappers.
Key Capabilities
- Multimodal Understanding: Processes both text and image inputs, enabling visual question answering and image description generation.
- Instruction Following: Fine-tuned to follow instructions for various tasks, making it adaptable to different prompts.
- Standard API Compatibility: Loadable and usable directly with
transformersandqwen-vl-utils, simplifying development and deployment. - Efficient Inference: With 3 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 interact with users using both text and visual information.
- Research and Development: Experimenting with multimodal large language models in a readily accessible format.