Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| Mamba (модел с отворено състояние)× | QLoRA× | |
|---|---|---|
| Област | Дълбоко обучение | Дълбоко обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване | 2023 | 2023 |
| Създател≠ | Albert Gu | Tim Dettmers |
| Тип≠ | Neural network architecture | Training methodology |
| Основополагащ източник≠ | Gu, A., & Dao, C. (2023). Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.08956. link ↗ | Dettmers, T., Pagnoni, A., Holtzman, A., & Contrastive, L. (2023). QLoRA: Efficient finetuning of quantized LLMs. arXiv preprint arXiv:2305.14314. link ↗ |
| Други названия≠ | Mamba, State space models, Selective state space | QLoRA, Quantized LoRA |
| Свързани | 4 | 4 |
| Резюме≠ | Mamba is a sequence model architecture introduced by Gu and Dao in 2023 that achieves linear-time complexity while maintaining strong performance on language modeling tasks. By combining state space models with input-dependent selectivity, Mamba addresses the quadratic complexity of transformers while preserving modeling power. | QLoRA is an efficient fine-tuning method introduced by Dettmers et al. in 2023 that enables fine-tuning large language models using quantization and low-rank adaptation. By combining 4-bit quantization with LoRA, QLoRA reduces memory requirements by 75%, enabling fine-tuning of 65B-parameter models on single GPUs. |
| ScholarGateНабор от данни ↗ |
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