AI: The LLM (70 billion) at Home
Home Lab Engineers can tinker with AI at -- Home ....
BY ML SHAW
Move over Blockchain/Coin mining! The diversification of the GPU will continue into the future.
As showcased by Answer.AI's latest breakthrough, the convergence of FSDP and QLoRA is revolutionizing the landscape of AI model training. By ingeniously combining these technologies, Answer.AI has unlocked the potential to train a 70 billion parameter language model on a regular desktop computer with just two gaming GPUs. This achievement not only democratizes access to state-of-the-art AI models but also empowers individuals and small labs to create their own personalized models, marking a significant step towards democratizing AI.
The collaboration between Answer.AI, Tim Dettmers, and Hugging Face has paved the way for a new era in AI research and development. By leveraging the computational power of consumer-grade GPUs and optimizing the training process through FSDP and QLoRA, researchers and enthusiasts alike can now embark on ambitious AI projects without the need for costly data center hardware. This democratization of AI technology aligns perfectly with Answer.AI's mission to make useful AI accessible to everyone, ushering in a future where innovation knows no bounds.
As we witness the dawn of this new era, it's clear that the impact of Answer.AI's breakthrough extends far beyond the realm of AI research. By lowering the barrier to entry for training large language models, this open-source system empowers individuals and organizations to explore new frontiers in natural language processing, paving the way for transformative applications across industries. With the democratization of AI training now within reach, the future of artificial intelligence is brighter and more inclusive than ever before.
The Short of it:
- Answer.AI released an open source system enabling efficient training of a 70 billion parameter language model on standard gaming GPUs.
- The system combines FSDP and QLoRA techniques.
- FSDP enables scaling model training across multiple GPUs by splitting model parameters.
- QLoRA quantizes models to reduce memory usage, making them trainable on GPUs with limited RAM.
- Collaboration between Answer.AI, Tim Dettmers, and Hugging Face facilitated this project.
- The system aims to democratize AI model creation by making large model training accessible to more people.
- HQQ, a combination of fast quantization and accurate optimization, enhances model accuracy and speed.
- Users can access the system via a repository and follow provided instructions for training on multiple GPUs.
- Continuous improvements and community feedback are expected to enhance the system's performance and accessibility.
Want to dive in?
source:
Answer.AI - You can now train a 70b language model at home
https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html