5,259,435 AI resources indexedWorking toward the world’s largest AI resource index.
Publications about AI methods and applications.
Research paper by b sh1vam, published 2026 in Zenodo (CERN European Organization for Nuclear Research). Research area: Quantum Computing Algorithms and Architecture.
Research paper by Hongyu Wang and collaborators. Published 2024-11-07. Topics: 1-bit Large Language Models (LLMs), BitNet b1.58, BitNet a4.8, hybrid quantization, sparsification.
Research paper by Shuming Ma and collaborators. Published 2025-04-16. Topics: BitNet, Large Language Model, 1-bit, 2-billion parameters, corpus.
Research paper by Xun Wu and collaborators. Published 2025-10-15. Topics: BitNet Distillation, BitDistill, SubLN, multi-head attention distillation, continual pre-training.
Research paper by Zhen Li and collaborators. arXiv record dated 2026-07-22. Research area: Language technology.
Research paper by Hongyu Wang and collaborators. Published 2025-04-25. Topics: Large Language Models, LLMS, BitNet, H-BitLinear, Hadamard transformation.
Research paper by Jinheng Wang and collaborators. arXiv record dated 2025-02-17. Research area: Machine learning.
Research paper by Hongyu Wang and collaborators. Published 2023-10-17. Topics: BitNet, 1-bit Transformer, BitLinear, nn.Linear, 1-bit weights.
Research paper by Jacob Nielsen and collaborators. arXiv record dated 2025-02-17. Research area: Machine learning.
Research paper by Sai Kiran Bathula. Published 2026-10-15. Topics: embedding table, principal-component projection, sign binarisation, BitNet b1.58, ternary.
This work presents a Fully BInarized Large Language Model (FBI-LLM), demonstrating for the first time how to train a large-scale binary language model from scratch (not the partial binary or ternary LLM like BitNet b1.58) to match the performance of its full-precision counterparts (e.g., FP16 or BF16) in transformer-based LLMs. It achieves this by employing an autoregressive distillation (AD) loss with maintaining equivalent model dimensions (130M, 1.3B, 7B) and training data volume as regular LLM pretraining, while delivering competitive results in terms of perplexity and task-specific effectiveness. Intriguingly, by analyzing the training t
Research paper by Shaohan Huang and collaborators. Published 2024-11-25. Topics: Multi-Head Mixture-of-Experts, MH-MoE, representation spaces, experts, FLOPs.
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