Gemma 4 & LLM Ops: Fine-Tuning, Local Inference, and VRAM Management

Gemma 4 & LLM Ops: Fine-Tuning, Local Inference, and VRAM Management Today's Highlights Today's top stories delve into practical challenges and solutions for local LLM development, from leveraging new fine-tuning libraries to optimizing performance for cutting-edge models on RTX GPUs. We cover critical llama.cpp updates, the stable release of TRL for RLHF, and deep-dive into Gemma 4's significant VRAM demands. TRL v1.0: Post-Training Library Built to Move with the Field (Hugging Face Blog) Source: https://huggingface.co/blog/trl-v1 TRL (Transformer Reinforcement Learning) has reached its 1.0 milestone, solidifying its position as a go-to library for fine-tuning large language models using Reinforcement Learning from Human Feedback (RLHF). This release marks a significant step in providing robust, flexible, and efficient tools for developers looking to customize LLMs. TRL v1.0 offers streamlined implementations of popular RLHF algorithms like PPO, DPO, and KTO, abstracting away much