Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transfe
所属插件包：emerging-techniques
Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ token
所属插件包：emerging-techniques
Merge multiple fine-tuned models using mergekit to combine capabilities without retraining. Use when creating specialized models by blending domain-specific exp
所属插件包：emerging-techniques
Reduce LLM size and accelerate inference using pruning techniques like Wanda and SparseGPT. Use when compressing models without retraining, achieving 50% sparsi
所属插件包：emerging-techniques
Train Mixture of Experts (MoE) models using DeepSpeed or HuggingFace. Use when training large-scale models with limited compute (5× cost reduction vs dense mode
所属插件包：emerging-techniques
Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× spe
所属插件包：emerging-techniques
