Gemma-3-R1984-27B
| Property | Value |
|---|---|
| Parameter Count | 27B |
| Context Window | 8,000 tokens |
| License | MIT (Agentic AI) / Gemma (gemma-3-27B) |
| Hardware Requirements | NVIDIA A100 GPU (53GB+ VRAM) |
| Model Path | VIDraft/Gemma-3-R1984-27B |
What is Gemma-3-R1984-27B?
Gemma-3-R1984-27B is an advanced agentic AI platform that builds upon Google's Gemma-3-27B foundation model. It's designed as a comprehensive solution that combines multimodal processing capabilities with deep research functionality through web search integration. This model stands out for its ability to handle long contexts up to 8,000 tokens and its focus on secure, local deployment using NVIDIA A100 GPUs.
Implementation Details
The model is architected for deployment on independent servers, requiring significant computational resources including an NVIDIA A100 GPU with at least 53GB VRAM. It implements state-of-the-art deep research capabilities through the SERPHouse API, allowing it to process up to 20 real-time search results for comprehensive analysis.
- Multimodal support for images (PNG, JPG, JPEG, GIF, WEBP), videos (MP4), and documents (PDF, CSV, TXT)
- Extended chain-of-thought reasoning for systematic answer generation
- Secure local deployment architecture preventing data leakage
- Integration with SERPHouse API for real-time web search capabilities
Core Capabilities
- Autonomous decision-making and independent action as an agentic AI platform
- Comprehensive multimodal processing across various file formats
- Deep research integration with explicit source citation
- Long-context handling up to 8,000 tokens
- Enhanced security through isolated local deployment
Frequently Asked Questions
Q: What makes this model unique?
The model's combination of multimodal processing, deep research capabilities, and secure local deployment sets it apart. Its ability to handle long contexts while maintaining high security standards makes it particularly suitable for sensitive enterprise applications.
Q: What are the recommended use cases?
The model excels in fast-response conversational agents, deep research and RAG applications, document comparison, visual question answering, and complex reasoning tasks. It's particularly well-suited for organizations requiring secure, locally-deployed AI solutions.
