Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU
What changed
trl with peft to make Large Language Model (LLM) fine-tuning with Reinforcement Learning more accessible to anyone! In this post, we explain why this is a competitive alternative to existing fine-tuning approaches. Note peft is a general tool that can be applied to many ML use-cases but it’s particularly interesting for RLHF as this method is especially memory-hungry!
Why it matters
A concrete addition to Practical AI: Tools, Models & Frameworks: it changes what's available to builders today rather than being general commentary.
How it compares
Related prior coverage to compare against:
- (LoRA) Fine-Tuning FLUX.1-dev on Consumer Hardware
- Fine-tuning now available for GPT-4o
- Fine-tuning LLMs to 1.58bit: extreme quantization made easy
Sources
- Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU (huggingface-blog)primary