Back to feed
research1197d ago
Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU
Researchers at Hugging Face developed a method to fine-tune 20B LLMs with RLHF on a 24GB consumer GPU. This approach enables efficient training of large models on limited hardware. The technique leverages parameter-efficient fine-tuning and offloading to disk. You can implement this method using Hugging Face's TRL and PEFT libraries.
Key takeaways
- Fine-tuning 20B LLMs possible on 24GB GPU.
- Uses parameter-efficient fine-tuning and disk offloading.
- Implemented with Hugging Face's TRL and PEFT libraries.
Researchers at Hugging Face developed a method to fine-tune 20B LLMs with RLHF on a 24GB consumer GPU. This approach enables efficient training of large models on limited hardware. The technique leverages parameter-efficient fine-tuning and offloading to disk. You can implement this method using Hugging Face's TRL and PEFT libraries.
Key takeaways
- Fine-tuning 20B LLMs possible on 24GB GPU.
- Uses parameter-efficient fine-tuning and disk offloading.
- Implemented with Hugging Face's TRL and PEFT libraries.