General-purpose Large Language Models (LLMs) are frequently fine-tuned through supervised fine-tuning (SFT) to enhance performance in specific domains. Better results can be achieved by distilling the chain-of-thought of a larger model at the cost of numerous expensive calls and a much greater amount of data.We propose a novel blueprint for efficient fine-tuning that uses reasoning only for complex data identified by entropy. Specifically, across three small open models (≈ 3B) we split the training data into complexity categories by a single token answer entropy (ROC AUC 0.73), fine-tune large language models (LLMs) via SFT and distillation, and show that our pipeline significantly outperforms the standard SFT approach (0.58 vs 0.45 average accuracy) and outperforms the distillation approach (0.58 vs 0.56 average accuracy) while using 81% less data.We publish our code and data to facilitate further research in this direction.

Modern LLM education is increasingly centered on system building: grounding generation with retrieval, enabling tool use, and deploying models under latency and cost constraints.We present an updated release of our open course on Transformer-based LLMs and multimodal models (Nikishina et al, 2024).The update introduces topics which became importance since the first edition, namely session on Retrieval Augmented Generation (RAG), a hands-on session on tool-using agents, an API-based track for applied work with LLM, and practical local inference with vLLM.We also add a dedicated session on multimodal dialog models with a focus on dialog grounding. We enriched the course with a discussion on long-context transformers, focusing on KV-cache efficiency along with the related models and benchmarks.All materials are released online.

This paper presents a course on neural networks based on the Transformer architecture targeted at diverse groups of people from academia and industry with experience in Python, Machine Learning, and Deep Learning but little or no experience with Transformers. The course covers a comprehensive overview of the Transformers NLP applications and their use for other data types. The course features 15 sessions, each consisting of a lecture and a practical part, and two homework assignments organized as CodaLab competitions. The first six sessions of the course are devoted to the Transformer and the variations of this architecture (e.g., encoders, decoders, encoder-decoders) as well as different techniques of model tuning. Subsequent sessions are devoted to multilingualism, multimodality (e.g., texts and images), efficiency, event sequences, and tabular data.We ran the course for different audiences: academic students and people from industry. The first run was held in 2022. During the subsequent iterations until 2024, it was constantly updated and extended with recently emerged findings on GPT-4, LLMs, RLHF, etc. Overall, it has been ran six times (four times in industry and twice in academia) and received positive feedback from academic and industry students.