Coralfil/Atlantis-Pyramid-32B
Coralfil/Atlantis-Pyramid-32B is a 32 billion parameter language model developed by Coralfil, fine-tuned from Qwen/Qwen2.5-32B-Instruct. Specialized for marine science, it excels in marine restoration, aquaculture chemistry, dynamic oceanography modeling, and coral reef ecosystem analysis. This model leverages a Coralfil-validated marine science corpus for its supervised fine-tuning. It is designed for research and operational use within the Coralfil OS ecosystem, providing expert-level insights in its domain.
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Atlantis-Pyramid-32B: Specialized Marine Science LLM
Atlantis-Pyramid-32B is Coralfil's flagship model, a 32 billion parameter language model fine-tuned from Qwen/Qwen2.5-32B-Instruct. It is uniquely specialized for advanced applications in marine science, leveraging a proprietary Coralfil-validated marine science corpus for its training.
Key Capabilities
- Domain Expertise: Highly proficient in marine restoration science, aquaculture chemistry, and dynamic oceanography modeling.
- Ecosystem Analysis: Excels at coral reef ecosystem analysis and interpretation of oceanographic data.
- Fine-Tuning: Utilizes LoRA SFT (r=16, alpha=32) on key transformer modules, trained in 4-bit precision and merged for efficient inference.
- Base Model: Built upon the robust architecture of Qwen2.5-32B-Instruct, ensuring strong foundational language understanding.
Good For
- Marine Biology Research: Answering complex questions and analyzing data related to marine biology.
- Aquaculture Optimization: Assisting in parameter optimization for aquaculture systems.
- Environmental Assessment: Supporting environmental compliance assessment and coral reef health monitoring.
- Oceanographic Interpretation: Interpreting complex oceanographic datasets for scientific and operational purposes.
This model is intended for research and operational use within the Coralfil OS ecosystem, offering specialized insights where general-purpose models may lack depth. Users should validate outputs for critical decisions due to its domain-specific specialization.