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Linganisha mbinu

Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.

Mamba (Muundo wa Nafasi ya Hali)×QLoRA×
NyanjaUjifunzaji wa KinaUjifunzaji wa Kina
FamiliaMachine learningMachine learning
Mwaka wa asili20232023
MwanzilishiAlbert GuTim Dettmers
AinaNeural network architectureTraining methodology
Chanzo asiliaGu, 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 ↗
Majina mbadalaMamba, State space models, Selective state spaceQLoRA, Quantized LoRA
Zinazohusiana44
MuhtasariMamba 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.
ScholarGateSeti ya data
  1. v1
  2. 1 Vyanzo
  3. PUBLISHED
  1. v1
  2. 1 Vyanzo
  3. PUBLISHED

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ScholarGateLinganisha mbinu: Mamba (State Space Model) · QLoRA. Imepatikana 2026-06-18 kutoka https://scholargate.app/sw/compare