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keshik6 / grafting

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[NeurIPS 2025 Oral] Official Code for Exploring Diffusion Transformer Designs via Grafting

image-generation post-training self-attention convolutions diffusion-models grafting linear-attention text-to-image-generation architecture-research diffusion-transformer sub-quadratic-attention model-grafting hyena-operator model-architecture-editing diffusion-transformers architecture-editing hyena-x hyena-y mamba-2
  • Updated Jan 9, 2026
  • Jupyter Notebook

HarmoniqOS / ssm-aware-lora-finetuning

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Systematic study of LoRA fine-tuning strategies for IBM Granite 4.0-H-Micro (Mamba-2 + Transformer hybrid). Demonstrates the impact of architecture-aware target selection and SSM core parameter co-training, including analysis of PEFT serialization behavior. Reports up to 37% relative improvement over LoRA-only baselines.

lora granite mamba state-space-model peft llama-cpp gguf parameter-efficient-fine-tuning llm-finetuning hybrid-architecture ibm-granite mamba-2 granite4
  • Updated Mar 1, 2026
  • Python

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