BrahmAI/yoda-llama-3.1-8B-instruct
Text Generation
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AI & ML interests
Foundational Models, AI for Science and Engineering. AI platform and Agenteic AIs
Recent Activity
Organization Card
BrahmAI
Science-Driven Foundation Models
Building foundation models through rigorous scientific principles and fundamental research.
Vision
BrahmAI develops foundation models that prioritize scientific understanding over empirical scaling. Our approach integrates principles from computational neuroscience, physics, mathematics, and cognitive science to create genuinely intelligent systems.
Approach
Core Principles
- Scientific Rigor: Every architectural decision grounded in empirical research
- Theoretical Foundations: Built on robust mathematical and computational frameworks
- Efficiency by Design: Optimizing for both performance and computational resources
- Interpretable Intelligence: Transparent and explainable decision-making processes
Research Areas
- Casual reasoning and understanding
- Information-theoretic optimization
- Multi-modal representation learning
- Compositional generalization
- Continual learning systems
Models
| Model | Focus Area | Status |
|---|---|---|
| BrahmAI-Core | General intelligence | Research |
| BrahmAI-Sci | Scientific reasoning | Research |
| BrahmAI-Code | Program synthesis | Research |
Capabilities
Target Domains
- Natural language understanding and generation
- Mathematical reasoning and theorem proving
- Code synthesis and analysis
- Scientific hypothesis generation
- Multi-modal processing
- Complex system modeling
Key Differentiators
- First-principles architectural design
- Reduced computational requirements for comparable performance
- Built-in alignment and safety mechanisms
- Cross-domain transfer capabilities
Technical
Architecture
Novel approaches to:
- Attention mechanisms
- Memory systems
- Representation learning
- Optimization dynamics
Infrastructure
- Distributed training framework
- Efficient inference systems
- Comprehensive evaluation suite
Resources
Collaboration
We collaborate with leading research institutions and organizations advancing the frontiers of artificial intelligence.
For research partnerships: [email protected]
For general inquiries: [email protected]
Team
Interdisciplinary team spanning:
- Machine Learning
- Theoretical Computer Science
- Computational Neuroscience
- Physics & Mathematics
- Systems Engineering
[](https://github.com/brahmai)
[](https://papers.brahmai.ai)
[](https://docs.brahmai.ai)
datasets
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