--- library_name: pytorch tags: - ai-research - language-models - efficient-ml - open-research - reasoning - representation-learning emoji: 🚀 pinned: false thumbnail: >- https://cdn-uploads.huggingface.co/production/uploads/67baefa4348a9fbc0016c9d2/fTEG-opSVyvbPMk60B-ag.png --- # Haipai Research ![Haipai Logo](haipai_logo.png) ## Overview **Haipai Research** is an independent AI research organization focused on developing **efficient, scalable, and mathematically grounded learning systems**. Our work emphasizes clarity of design, strong inductive biases, and open experimentation, particularly in regimes where compute and data are constrained. We aim to explore alternatives to standard large-scale training paradigms by combining **theoretical insight** with **practical model-building**. --- ## Research Focus Our primary research directions include: - Efficient language models and small-to-mid scale architectures - Alternative learning rules beyond standard backpropagation - Representation learning and structured reasoning - Mathematical foundations of deep learning - Model compression, quantization, and optimization - Architecture-level efficiency and memory-aware design --- ## Philosophy We believe that progress in AI does not come solely from scale, but from: - better inductive structure - principled training dynamics - transparent and reproducible research Our goal is to build systems that are **understandable, efficient, and robust**, while remaining fully open to the research community. --- ## Open Research & Reproducibility - All models, code, and experiments are released openly when possible - Results are reported with clear assumptions and limitations - Emphasis on ablations, comparisons, and reproducibility --- ## Intended Use Models released under Haipai Research are intended for: - academic research - experimentation and benchmarking - educational and exploratory purposes They are **not** intended for high-risk or safety-critical applications without further evaluation. --- ## Limitations - Models may be trained on limited data or compute - Experimental architectures may not be fully optimized - Results should be interpreted as research findings, not production guarantees --- ## Contact For collaboration, discussion, or research questions: - Hugging Face organization page - Associated repositories and papers linked per model --- ## Citation If you use models or ideas from Haipai Research in your work, please cite the corresponding repository or paper when available.