Qika/DeepMedix-R1
Qika/DeepMedix-R1 is a 7 billion parameter vision-language model (VLM) based on the Qwen2.5-VL architecture, developed by Qika. This model is designed for multimodal reasoning, integrating both text and image inputs with a context length of 32768 tokens. It specializes in tasks requiring detailed visual analysis and structured reasoning, particularly by leveraging bounding box coordinates for local region analysis. DeepMedix-R1 is optimized for generating precise answers following an internal monologue reasoning process.
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Qika/DeepMedix-R1: A Vision-Language Model for Detailed Reasoning
Qika/DeepMedix-R1 is a 7 billion parameter vision-language model (VLM) built upon the Qwen2.5-VL architecture. It is engineered to process and reason over both textual and visual information, supporting a substantial context length of 32768 tokens.
Key Capabilities
- Multimodal Input Processing: Seamlessly integrates image and text inputs for comprehensive understanding.
- Structured Reasoning: Employs an internal monologue (
<think> </think>) to formulate reasoning steps before providing a final answer. - Local Region Analysis: Prioritizes and leverages bounding box coordinates (
[x_min, y_min, x_max, y_max]) for in-depth analysis of specific image regions during its reasoning process. - Instruction Following: Designed to adhere to specific output formats, including enclosing final answers in
\boxed{}.
Good For
- Applications requiring detailed visual question answering and reasoning.
- Tasks where precise analysis of image sub-regions is critical.
- Use cases benefiting from a structured, step-by-step reasoning process for explainability or accuracy.
- Scenarios needing a VLM with a large context window for complex multimodal inputs.