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