klotz: rlhf*

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  1. - 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.
  2. 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.

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