karakuri-ai/karakuri-vl-32b-thinking-2507-exp
The karakuri-ai/karakuri-vl-32b-thinking-2507-exp is an experimental 32 billion parameter Vision-Language Model developed by KARAKURI Inc. It is designed to generate explicit reasoning traces within tags before producing a final answer, enhancing transparency in its decision-making process. This model supports both Japanese and English and is fine-tuned from KARAKURI VL 32B Instruct 2507, making it suitable for complex multimodal reasoning tasks.
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KARAKURI VL 32B Thinking 2507 Experimental
This model, developed by KARAKURI Inc., is an experimental 32 billion parameter Vision-Language Model (VLM) that distinguishes itself by generating internal reasoning steps. Before providing a final answer, the model outputs its thought process within <think> tags, offering insight into its decision-making. It is fine-tuned from the KARAKURI VL 32B Instruct 2507 model and supports both Japanese and English.
Key Capabilities
- Explicit Reasoning: Generates detailed reasoning traces within
<think>tags, allowing users to observe the model's thought process. - Multimodal Understanding: As a Vision-Language Model, it can process and reason about both image and text inputs.
- Bilingual Support: Operates effectively in both Japanese and English.
- Customizable System Prompt: Designed to work with a specific system prompt that guides its reasoning and response structure, which can be customized for various use cases.
Good For
- Complex Reasoning Tasks: Ideal for applications requiring transparent and verifiable AI reasoning, where understanding "how" the model arrived at an answer is crucial.
- Debugging and Analysis: Useful for developers and researchers to analyze model behavior and identify potential biases or errors in its thought process.
- Educational Tools: Can be leveraged in educational contexts to demonstrate logical thinking and problem-solving steps.
Note: As an experimental model, it may occasionally produce incomplete responses or unclosed <think> tags. Users are strongly recommended to use the provided system prompt to stabilize its behavior.