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Tags: rlhf* + llm*

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1. 6 Common LLM Customization Strategies Briefly Explained

   The article explains six essential strategies for customizing Large Language Models (LLMs) to better meet specific business needs or domain requirements. These strategies include Prompt Engineering, Decoding and Sampling Strategy, Retrieval Augmented Generation (RAG), Agent, Fine-Tuning, and Reinforcement Learning from Human Feedback (RLHF). Each strategy is described with its benefits, limitations, and implementation approaches to align LLMs with specific objectives.

   2025-02-25 Tags: llm, prompt engineering, rag, agent, fine-tuning, rlhf by klotz
2. How to train your large language model: A new technique speeds up the process

   This article discusses the process of training a large language model (LLM) using reinforcement learning from human feedback (RLHF) and a new alternative method called Direct Preference Optimization (DPO). The article explains how these methods help align the LLM with human expectations and make it more efficient.

   2024-05-15 Tags: llm, reinforcement learning, human feedback, openai, chatgpt, rlhf, dpo, training by klotz
3. 14 Free Large Language Models Fine-Tuning Notebooks

   • 14 free colab notebooks providing hands-on experience in fine-tuning large language models (LLMs).
   • The notebooks cover topics from efficient training methodologies like LoRA and Hugging Face to specialized models such as Llama, Guanaco, and Falcon.
   • They also include advanced techniques like PEFT Finetune, Bloom-560m-tagger, and Meta_OPT-6–1b_Model.

   2024-02-10 Tags: llm, lora, hugging face, llama, guanaco, falcon, peft, fine tune, mpt-instruct, phi, self-supervised, rlhf by klotz

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