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fp16-training

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Md-Emon-Hasan / Fine-Tuning

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End-to-end fine-tuning of Hugging Face models using LoRA, QLoRA, quantization, and PEFT techniques. Optimized for low-memory with efficient model deployment

nlp machine-learning natural-language-processing deep-learning transformers pytorch lora quantization model-training fine-tuning peft gpu-optimization huggingface gradient-checkpointing huggingface-datasets qlora bitsandbytes parameter-efficient-fine-tuning low-memory-training fp16-training
  • Updated Dec 27, 2025
  • Jupyter Notebook

krishnakoushik225 / GenDiff-PEFT-Efficient-Conditional-Diffusion-Optimization

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Parameter-efficient optimization of conditional diffusion models using multi-resolution attention, classifier-free guidance ablation, and DDIM sampling — achieving 17% FID improvement with 85% reduced training time.

computer-vision deep-learning pytorch transfer-learning unet attention-mechanism cifar10 fid inception-score diffusion-models pytorch-lightning conditional-generation ddim generative-ai classifier-free-guidance fp16-training parameter-efficient-training
  • Updated Mar 3, 2026
  • Jupyter Notebook

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