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Preview of the QVQ model featuring 72 billion parameters for enhanced performance.

Instruction-based variation of the Qwen2 model for visionary tasks.

Vision-instruct version of Llama with 11 billion parameters.

Tiny version of the DeepSeek model designed for limited resources.

Preview version of the QVQ model with a GGUF framework.

Large visual understanding AI from Microsoft.

Vision language model for enhanced interactions.

OCR model designed for text extraction.

Updated version of the DeepSeek model enhancing visual learning.

12B image-focused model designed for enhanced image generation.

Compact version for instructive language tasks, balancing efficiency and performance.

9 billion parameter cognitive agent model from THUDM.

Integrates visual understanding with written language for comprehensive insights.

Generative model emphasizing multilingual capabilities and quality interactions.

Combines vision and instruction for enhanced interactive capabilities.

Input understanding model specialized in multi-format handling.

Korean language model emphasizing cultural relevance in communication.

Dreamlike model designed for creative and surreal outputs.

Compact model targeting efficient language tasks with minimal overhead.

Integrates pixel-based understanding in narrative construction.

72B instructive model focusing on visual tasks.

An intern-level model tailored for high-stakes visual language tasks.

Supports optical character recognition processes with language integration.

An internal model for extensive visual learning protocols.

Versatile model trained with a focus on language and visual tasks.

Able to serve specialized instructions focused on visual tasks.

A variant of Qwen specializing in visual language tasks.

7B version aimed at enhancing visual understanding through interactions.

A multimodal model for biomedical conversations.

A newer version of the Llama model with enhanced capabilities.

A compact intern-level version designed for flexibility and efficiency.

A larger internal model enhancing visual content understanding.

An extensive model designed for advanced visual tasks.

Small-scale yet efficient for visual and language processing tasks.

Multimodal model aimed at real-time processing of visual and textual data.

Focuses on vision-language tasks, improving multi-modal AI interactions.

Combines language and visual understanding for contextualized interactions.

A robust model designed for information extraction tasks.

OCR model aimed at extracting information from printed documents.

A transformer model for enhanced language understanding.

Chat-oriented model optimized for interactive dialogues.

A specialized medical model for language understanding.

A foundational model built for multi-modal interactions.

A compact model focusing on tasks within very limited data environments.

A large multi-modal model designed for intricate tasks.

Model for language understanding and molecular tasks.

Designed for molecular understanding and chemical structure generation.

A high-capacity model optimized for specific domain tasks.

Vision-oriented instruction-following model built for enhanced image-based tasks.

A customized model focusing on document comprehension using advanced AI methods.

Enhanced version targeting larger datasets and more complex language tasks.

An advanced variant of the Paligemma model for better outputs.

Targets instruction-driven tasks across various conversational contexts.

Aimed at biomedical applications with enhanced processing capabilities.

Versatile model enabling multiple operational tasks and analyses.

A preview model for quick exploratory tasks.

Instruction model for envisioning with 7 billion parameters.

A conversational model with lyrical and artistic dialogue.

A flexible model aiming for intuitive processing within language tasks.

Model focusing on mathematical processing with instructions.

Targeted towards data interpretation and extraction tasks.

Advanced variant targeting enhanced processing of visual inputs.

Preview version focusing on efficient processing for Chinese language tasks.

Designed for user interfaces focusing on interactive generative tasks.

Preview model focusing on generating higher data efficiency.

Targets preview operations for language processing applications.

A model for generating lyrical and artistic text.

An internal model focused on innovative visual learning.

A high-definition model for enhancing visual content.

A binarized model for efficient processing.

Focused on processing and understanding invoices and receipts through OCR.

Guides interactions using multi-modal capabilities for enhanced responses.

Vision-centric version enhancing visual comprehension.

Designed for operational support and task automation in various environments.

A high-capacity model for advanced tasks and complex visual language applications.

A versatile model designed for general purpose text handling with instruction capabilities.

A high-capacity model focusing on large-scale language understanding.

A preview model for assessing capabilities across functionalities.