The article also introduces LoRA Land, a collection of over 25 fine-tuned Mistral-7b models that surpass GPT-4 in task-specific applications. It announces the first purely serverless solution for fine-tuned LLMs, which allows users to query their models without a dedicated GPU deployment. Lastly, it presents the LoRA Exchange (LoRAX), an open-source framework for serving hundreds of fine-tuned LLMs at the cost of one GPU with minimal degradation in throughput and latency.
Key takeaways:
- LoRA Land is a collection of 25+ fine-tuned Mistral-7b models that outperform GPT-4 in task-specific applications, offering a blueprint for teams seeking to efficiently and cost-effectively deploy AI systems.
- Introducing the first purely serverless solution for fine-tuned LLMs, Serverless Fine-tuned Endpoints, which allow users to query their fine-tuned LLMs without spinning up a dedicated GPU deployment.
- LoRAX, the Open Source Framework for Serving 100s of Fine-Tuned LLMs in Production, has been released to the open-source community, making it possible to serve hundreds of fine-tuned LLMs at the cost of one GPU with minimal degradation in throughput and latency.
- State-of-the-art fine-tuning techniques such as quantization, low-rank adaptation, and memory-efficient distributed training are combined with right-sized compute to ensure jobs are successfully trained as efficiently as possible.