This article details the process of fine-tuning a 135M-parameter model for summarization using reinforcement learning from human feedback (RLHF). It covers the implementation of supervised fine-tuning (SFT), reward modeling, and two key RLHF algorithms: Proximal Policy Optimization (PPO) and Direct Preference Optimization (DPO). The author explains how this pipeline was recreated on a single free GPU, with all algorithms written in plain Python. AI
IMPACT Details a practical approach to fine-tuning smaller models for summarization tasks using accessible resources.
RANK_REASON The item describes a technical process for fine-tuning a language model using specific machine learning techniques. [lever_c_demoted from research: ic=1 ai=1.0]
Read on Medium — fine-tuning tag →
- 135M-Parameter Model
- Direct Preference Optimization
- Proximal Policy Optimization
- reinforcement learning from human feedback
- Reward Modelling
- supervised fine-tuning
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