Large language model training with 90% cost-cut fine-tuning

Context Tuning: Efficient LLM Adaptation via Direct Memory Representation Optimization

This paper introduces "Context Tuning," a novel method for efficiently adapting large language models (LLMs) by directly optimizing their me

A Journey from AI to LLMs and MCP - 3 - Boosting LLM Performance – Fine-Tuning, Prompt Engineering, and RAG

Fine-Tuned Small LLMs Outperform Larger Models at 5-30x Lower Cost with Data Curation

The article discusses how fine-tuned small language models (LLMs) can outperform larger ones at significantly lower costs (5-30x) through pr

How to Fine-tune Large Language Models (LLMs) for Specialized Applications

Data Engineering for Large Language Models: Architecture, Algorithms and Projects

This is a technical book about data engineering for large language models (LLMs), covering the complete technical stack from pre-training da

LK Losses: A New Training Objective to Optimize Acceptance Rate in Speculative Decoding for LLMs

This paper introduces LK losses, a novel training objective for speculative decoding in large language models (LLMs). Speculative decoding a