- [P] Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adapters https://sebastianraschka.com/blog/2023/llm-finetuning-llama-adapter.html 4 comments machinelearning
Linking pages
Linked pages
- [2005.14165] Language Models are Few-Shot Learners https://arxiv.org/abs/2005.14165 201 comments
- GitHub - Lightning-AI/lit-llama: Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed. https://github.com/Lightning-AI/lit-llama 69 comments
- [2303.16199] LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention https://arxiv.org/abs/2303.16199 52 comments
- [1810.04805] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding https://arxiv.org/abs/1810.04805 18 comments
- Machine Learning Q… by Sebastian Raschka, PhD [PDF/iPad/Kindle] https://leanpub.com/machine-learning-q-and-ai 12 comments
- https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf 1 comment
- GitHub - ZrrSkywalker/LLaMA-Adapter: Fine-tuning LLaMA to follow Instructions within 1 Hour and 1.2M Parameters https://github.com/ZrrSkywalker/LLaMA-Adapter 1 comment
- [2302.13971] LLaMA: Open and Efficient Foundation Language Models https://arxiv.org/abs/2302.13971 0 comments
Would you like to stay up to date with Computer science? Checkout Computer science
Weekly.
Related searches:
Search whole site: site:sebastianraschka.com
Search title: Understanding Parameter-Efficient Finetuning of Large Language Models: From Prefix Tuning to LLaMA-Adapters
See how to search.