supervised-fine-tuning

Large language models (LLMs) are rapidly evolving from easy text prediction systems into advanced reasoning engines able to tackling complex challenges. Initially designed to predict the following word in a sentence, these models have...

Introduction In a YouTube video titled , former Senior Director of AI at Tesla, Andrej Karpathy discusses the psychology of Large Language Models (LLMs) as emergent cognitive effects of the training pipeline. This text is inspired by his...

Current long-context large language models (LLMs) can process inputs as much as 100,000 tokens, yet they struggle to generate outputs exceeding even a modest length of two,000 words. Controlled experiments reveal that the model’s...

Owing to its robust performance and broad applicability when put next to other methods, LoRA or Low-Rank Adaption is some of the popular PEFT or Parameter Efficient Fantastic-Tuning methods for fine-tuning a big language...

Because the applications of enormous language models expand into specialized domains, the necessity for efficient and effective adaptation techniques becomes increasingly crucial. Enter RAFT (Retrieval Augmented High quality Tuning), a novel approach that mixes...