Main points:
1. Fine-tuning effectively adapts pre-trained language models (LLMs) to specific tasks or domains.
2. Parameter-efficient fine-tuning (PEFT) techniques like Adapters and prompt-tuning improve efficiency by updating only a subset of model parameters.
3. Storing and loading individual fine-tuned LLMs for each task can be computationally inefficient due to large memory requirements.
4. Strategies such as model parallelism, pipeline parallelism, and data parallelism efficiently train LLMs across multiple devices and compute nodes.
5. Efficient attention mechanisms, like nested attention and linear attention, reduce memory and compute requirements for processing long inputs.
6. The choice of positional encoding scheme affects the ability of positional embeddings to inject positional information into LLMs and generalize to longer sequences.
7. Techniques such as pruning and quantization reduce the memory footprint and computational requirements of LLMs.
8. FasterTransformer and other libraries provide efficient implementations of LLMs and facilitate distributed training and inference.
9. To handle longer inputs effectively, techniques like efficient attention mechanisms, advanced positional embeddings, and transformer alternatives address the limitations of long context lengths.
Reference: https://arxiv.org/abs/2307.10169