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1. Fine-tune a Mistral-7b model with Direct Preference Optimization

   Boost the performance of your supervised fine-tuned models

   2024-01-02 Tags: llm, dpo, mistral by klotz
2. How Good Are the Latest Open LLMs? And Is DPO Better Than PPO?

   This article discusses the latest open LLM (large language model) releases, including Mixtral 8x22B, Meta AI's Llama 3, and Microsoft's Phi-3, and compares their performance on the MMLU benchmark. It also talks about Apple's OpenELM and its efficient language model family with an open-source training and inference framework. The article also explores the use of PPO and DPO algorithms for instruction finetuning and alignment in LLMs.

   2024-05-13 Tags: llms, mixtral, mixtral 8x22b, llama 3, phi-3, openelm, ppo, dpo, reinforcement learning, human feedback by klotz
3. 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
4. NousResearch Released Nous-Hermes-2-Mixtral-8x7B: An Open-Source LLM with SFT and DPO Versions

   Trained on a vast dataset comprising primarily GPT-4 generated data and supplemented with high-quality information from open datasets in the AI field, this model exhibits exceptional performance across various tasks. It introduces a novel SFT + DPO version, and for those who prefer a different approach, an SFT-only version is also made available

   2024-01-26 Tags: llm, nous, dpo, sft by klotz
5. Preference Tuning LLMs with Direct Preference Optimization Methods

   2024-01-18 Tags: llm, dpo, fine tuning, huggingface by klotz

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