Hacker News
- Implementing Weight-Decomposed Low-Rank Adaptation (DoRA) from Scratch https://magazine.sebastianraschka.com/p/lora-and-dora-from-scratch 10 comments
- Coding Self-Attention, Multi-Head Attention, Cross-Attention, Causal-Attention https://magazine.sebastianraschka.com/p/understanding-and-coding-self-attention 11 comments
- Ten Noteworthy AI Research Papers of 2023 https://magazine.sebastianraschka.com/p/10-ai-research-papers-2023 19 comments
- Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation) https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms 27 comments
- AI and Open Source in 2023 https://magazine.sebastianraschka.com/p/ai-and-open-source-in-2023 67 comments
- Training and aligning LLMs with RLHF and RLHF alternatives https://magazine.sebastianraschka.com/p/llm-training-rlhf-and-its-alternatives 14 comments
- Understanding Llama 2 and the New Code Llama LLMs https://magazine.sebastianraschka.com/p/ahead-of-ai-11-new-foundation-models 34 comments
- Why the original transformer figure is wrong, and some other tidbits about LLMs https://magazine.sebastianraschka.com/p/why-the-original-transformer-figure 49 comments
- Finetuning Large Language Models https://magazine.sebastianraschka.com/p/finetuning-large-language-models 70 comments
- Understanding large language models: A cross-section of the relevant literature https://magazine.sebastianraschka.com/p/understanding-large-language-models 31 comments
- [P] Ten Noteworthy AI Research Papers of 2023 https://magazine.sebastianraschka.com/p/10-ai-research-papers-2023 5 comments machinelearning
- [P] Practical Tips for Finetuning LLMs Using LoRA (Low-Rank Adaptation): Things I Learned From Hundreds of Experiments https://magazine.sebastianraschka.com/p/practical-tips-for-finetuning-llms 10 comments machinelearning
- [P] Deep Dive and Experiments for the NN + Gzip Method vs LLMs https://magazine.sebastianraschka.com/p/large-language-models-and-nearest 3 comments machinelearning
- [P] 4 Computer Vision Research Highlights in 2023 https://magazine.sebastianraschka.com/p/ahead-of-ai-10-state-of-computer 3 comments machinelearning
- Understanding Encoder And Decoder LLMs https://magazine.sebastianraschka.com/p/understanding-encoder-and-decoder 2 comments learnmachinelearning
- [P] Research Paper Highlights from May to June 2023 https://magazine.sebastianraschka.com/p/ai-research-highlights-in-3-sentences-2a1 3 comments machinelearning
- [P] Recapping recent LLM research concerning tuning strategies & data efficiency https://magazine.sebastianraschka.com/p/ahead-of-ai-9-llm-tuning-and-dataset/ 4 comments machinelearning
- [P] Why the Original Transformer Figure Is Wrong, And Some Other Interesting Tidbits https://magazine.sebastianraschka.com/p/why-the-original-transformer-figure 11 comments machinelearning
- [P] Finetuning LLMs Efficiently with Adapters https://magazine.sebastianraschka.com/p/finetuning-llms-with-adapters 2 comments machinelearning
- [P] 22 Research Paper Highlights (April-May 2023) -- Summarized In 3 Sentences Or Less https://magazine.sebastianraschka.com/p/ai-research-highlights-in-3-sentences 4 comments machinelearning
- Understanding Large Language Models -- A Cross-Section of the Most Relevant Literature To Get Up to Speed https://magazine.sebastianraschka.com/p/understanding-large-language-models 4 comments learnmachinelearning
- [P] Understanding Large Language Models -- a collection of the most relevant papers https://magazine.sebastianraschka.com/p/understanding-large-language-models 18 comments machinelearning
- Finetuning Large Language Models -- An introduction to the core ideas and approaches https://magazine.sebastianraschka.com/p/finetuning-large-language-models 2 comments learnmachinelearning